filter by: Publication Year
(Descending) Articles
Information Sciences (00200255) 692
Open relation extraction is a critical task in natural language processing aimed at automatically extracting relations between entities in open-domain corpora. Most existing systems focus on extracting binary relations (relations between two entities) while extracting more complex n-ary relations (involving more than two entities) remains a significant challenge. Additionally, many previous systems rely on hand-crafted patterns and natural language processing tools, which result in error accumulation and reduced accuracy. The current study proposes a novel approach to open n-ary relation extraction that leverages recent advancements in deep learning architectures. This approach addresses the limitations of existing open relation extraction systems, particularly their reliance on hand-crafted patterns and their focus on binary relations. It utilizes SpanBERT to capture relational patterns from text data directly and introduces entity embedding vectors to create distinct representations of entities within sentences. These vectors enhance the proposed system's understanding of the entities within the input sentence, leading to more accurate relation extraction. Notably, the proposed system in the present study achieves an F1-score of 89.79 and 92.67 on the LSOIE-wiki and OpenIE4 datasets, outperforming the best existing models by over 12% and 10%, respectively. These results highlight the effectiveness of the proposed approach in addressing the challenges of open n-ary relation extraction. © 2024 Elsevier Inc.
Neurocomputing (09252312) 616
Open Information Extraction (Open IE) is the task of identifying structured and machine-readable information from natural language text within an open domain context. This research area has gained significant importance in the field of natural language processing (NLP), attracting considerable attention for its potential to extract valuable information from unstructured textual data. Previous investigations heavily relied on manual extraction patterns and various NLP tools. While these methods often produce errors that accumulate and propagate throughout the systems, ultimately affecting the accuracy of the results. Moreover, recent Open IE studies have focused on extracting binary relations involving two entities. However, these binary approaches occasionally lead to the omission of essential information in the text, preventing a deeper comprehension of the content. This limitation arises from the fact that real-world relations often involve multiple entities, but binary approaches may oversimplify these relations and miss additional details crucial for a thorough understanding of text. To address these challenges, our study introduces an innovative system called “Open N-ary Information EXtraction (ONIEX).” This system incorporates two novel techniques: multihead relation attention mechanism and relation embedding. Multihead relation attention, in combination with relation embedding, enables the system to focus on relations extracted through the SpanBERT model and accurately identify associated entities for each relation. The ONIEX system's superior performance is substantiated through extensive experiments conducted on the OpenIE4 and LSOIE datasets, benchmark datasets for Open n-ary Information Extraction (Open n-ary IE). The results demonstrate the superiority of the ONIEX system over the existing state-of-the-art systems. © 2024 Elsevier B.V.
Expert Systems with Applications (09574174) 275
Entity search websites enable consumers to purchase products and entities. These websites commonly provide predefined search filters and contain entity information and user-created reviews. Consumers commonly exploit filters to eliminate irrelevant entities. Consequently, they explore reviews about the purified entities to identify an entity centered on their preferences. Inspired by this consumer-centric process, this paper introduces a mechanized model for entity search. In the presented model, consumer preferences are considered as queries comprising configurations for predefined filters and a textual part containing more explanations and constrains. Previous works on entity retrieval have not employed capabilities of structured entity information, and complete potential of insights in user reviews. In contrast, the presented model, considering this limitations, exploits structured entity information of predefined_slots to filter out many irrelevant entities. Consequently, it ranks entities by comparing text_part of consumer preferences with the entire core content of user reviews, indicating the order of corresponding entities centered on consumer preferences. The entity search model presented in this paper employs cutting-edge natural language processing (NLP) methods. Moreover, this paper introduces a model to curate entity search dataset, considering structured entity information and objective information in user reviews and exploit it to create a dataset by extracting and labeling restaurant information from Tripadvisor. The created entity search model incorporates a monoBERT-based text_ranker, fine-tuned employing the created training dataset, and evaluations indicate perceptible improvements in mean reciprocal rank (MRR), mean average precision (MAP), and normalized discounted cumulative gain (nDCG). © 2025 Elsevier Ltd
PLoS ONE (19326203) 20(5 May)
The extraction of subjective comparative relations is essential in the field of question answering systems, playing a crucial role in accurately interpreting and addressing complex questions. To tackle this challenge, we propose the SCQRE model, specifically designed to extract subjective comparative relations from questions by focusing on entities, aspects, constraints, and preferences. Our approach leverages multi-task learning, the Natural Language Inference (NLI) paradigm, and a specialized adapter integrated into RoBERTa_base_go_emotions to enhance performance in Element Extraction (EE), Compared Elements Identification (CEI), and Comparative Preference Classification (CPC). Key innovations include handling X- and XOR-type preferences, capturing implicit comparative nuances, and the robust extraction of constraints often neglected in existing models. We also introduce the Smartphone-SCQRE dataset, along with another domain-specific dataset, BrandsCompSent-19-SCQRE, both structured as subjective comparative questions. Experimental results demonstrate that our model outperforms existing approaches across multiple question-level and sentence-level datasets and surpasses recent language models, such as GPT-3.5-turbo-0613, Llama-2-70b-chat, and Qwen-1.5-7B-Chat, showcasing its effectiveness in question-based comparative relation extraction. © 2025 Babaali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries. ©2024 IEEE.
Applied Soft Computing (15684946) 156
Personalization of game difficulty is a critical task in leveraging artificial intelligence (AI) technologies to enhance player engagement in virtual worlds like metaverse. One of the key challenges in this area is developing methods for assessing a player's perception of game difficulty. This information can be used to dynamically adjust the game difficulty to match the player's skill level and preferences, which can improve the player's experience and engagement. The existing approaches have limitations such as relying on costly external devices, requiring time-consuming feedback or questionnaires, and being specific to certain game genres and narratives. In this paper, we propose a new method called ChatDDA for evaluating a player's perception of game difficulty by analyzing the content of their chat messages. Our method uses a pre-trained language model to extract semantic features from the chat messages, which are then used to train a feed-forward neural network to predict the player's level of hopefulness or despair about succeeding in the game. Three pre-trained language models—BERT, RoBERTa, and Twitter-roBERTa—are fine-tuned on a purpose-built dataset of player chat messages of the popular multiplayer online game PlayerUnknown's Battlegrounds (PUBG) tagged as expressing optimism or pessimism regarding game success. The results showed that our method can accurately predict a player's perception of game difficulty, with an accuracy of 0.953 on the test dataset of player chat messages. This suggests that our method has the potential to enhance player engagement and immersion within the game, ultimately leading to more satisfying and enjoyable metaverse experiences. © 2024 Elsevier B.V.
PLoS ONE (19326203) 19(5 May)
In the domain of question subjectivity classification, there exists a need for detailed datasets that can foster advancements in Automatic Subjective Question Answering (ASQA) systems. Addressing the prevailing research gaps, this paper introduces the Fine-Grained Question Subjectivity Dataset (FQSD), which comprises 10,000 questions. The dataset distinguishes between subjective and objective questions and offers additional categorizations such as Subjective-types (Target, Attitude, Reason, Yes/No, None) and Comparison-form (Single, Comparative). Annotation reliability was confirmed via robust evaluation techniques, yielding a Fleiss's Kappa score of 0.76 and Pearson correlation values up to 0.80 among three annotators. We benchmarked FQSD against existing datasets such as (Yu, Zha, and Chua 2012), SubjQA (Bjerva 2020), and ConvEx-DS (Hernandez-Bocanegra 2021). Our dataset excelled in scale, linguistic diversity, and syntactic complexity, establishing a new standard for future research. We employed visual methodologies to provide a nuanced understanding of the dataset and its classes. Utilizing transformer-based models like BERT, XLNET, and RoBERTa for validation, RoBERTa achieved an outstanding F1-score of 97%, confirming the dataset's efficacy for the advanced subjectivity classification task. Furthermore, we utilized Local Interpretable Model-agnostic Explanations (LIME) to elucidate model decision-making, ensuring transparent and reliable model predictions in subjectivity classification tasks. Copyright: © 2024 Babaali et al.
Multimedia Tools and Applications (13807501) 83(10)pp. 31049-31079
The game industry is witnessing a significant trend of players toward massively multiplayer online games (MMO). Players are keen on forming teams and cooperating/competing in these games. Real-time measurement of players’ performance is one of the subjects of researchers’ attention to dynamically adjust the game difficulty and immerse players in the game. However, our extensive studies show that real-time measuring of teams’ skill levels has received much less attention. In this paper, a general real-time method called DeepSkill is proposed to measure the MMOs teams’ skills directly using players’ gameplay raw low-level data. The proposed method, which is based on the evidence-centered assessment design, was tested under six different configurations using popular machine learning techniques, including deep neural network (DNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), CatBoost, random forest (RF), and linear support vector regression (LinearSVR). According to the results, the proposed method provides accurate skill estimations and expertise level classifications. Specifically, Deepskill’s DNN-based evidence model provided the lowest mean absolute error of 0.09 in team skill estimation. Additionally, the proposed method achieved an accuracy of 0.973 in classifying the teams’ expertise level for the expert-novice classification task. Furthermore, a cost-effectiveness analysis was performed on the two top-performing evidence models. The LightGBM-based evidence model yielded the best results in both training and prediction phases in terms of low resource consumption alongside considerable accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Large language models excel in various natural language processing tasks but often struggle with knowledge-intensive queries, particularly those involve rare entities or require precise factual information. This paper presents a novel framework that enhances capabilities of an LLM-based question answering system by incorporating structured knowledge from knowledge graphs. Our approach employs entity extraction, semantic similarity scoring, and adaptive graph exploration to efficiently navigate and extract relevant information from knowledge graphs. The core of the presented solution is a knowledge graph-enhanced language model process that iteratively refines subgraph exploration and answer generation, complemented by a fallback mechanism for robustness across diverse question types. Experiments on location-based questions from the Entity Questions dataset demonstrate significant improvements in the quality of responses. Using the Gemini 1.5 Flash model, our system achieved an accuracy increase from 36% to 71% for partially correct answers and from 22% to 69% for exactly correct answers, as evaluated by human assessors. This approach offers a promising direction for developing more reliable and accurate question answering systems, particularly for queries involving long-tail entities or specific factual knowledge. © 2024 IEEE.
Computers in Human Behavior (07475632) 151
Assessing players’ learning experiences in a proper manner is a fundamental aspect of successful game-based learning programs. One notable characteristic of these programs is stealth assessment, which involves integrating formative assessment into the learning environment without disrupting the learning process. In multiplayer online games (MOGs), the in-game online chat system is a commonly used tool that enables players to communicate through text or voice messages during gameplay. However, there is a lack of specific research on incorporating players’ in-game chat content for computational learning experience assessment, which could enhance the validity of stealth assessment. This study proposes a stealth assessment method based on natural language processing to highlight the significance of players’ in-game chat data in estimating learners’ skills in MOGs. A natural language processing model is developed using a distilled version of the Google BERT pre-trained model. The evaluations demonstrate that the proposed method accurately estimates a player's skill level by analyzing a few chat messages from the player. This method has the potential to make a profound impact on the field of game-based learning by enabling more precise assessment and supporting the design of tailored interventions and adaptive learning systems. This study pioneers computational skill assessment through chats in MOGs, opening up new opportunities for future investigations in skill assessment and having the potential to transform the field of game-based learning. © 2023 Elsevier Ltd
Hosseini, H. ,
Zare, M.S. ,
Mohammadi, A.H. ,
Kazemi, A. ,
Zojaji, Z. ,
Nematbakhsh, M.A. pp. 272-278
Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian. © 2024 IEEE.
Engineering Applications of Artificial Intelligence (09521976) 126
COVID-19 is a huge global challenge, on which numerous people and resources all over the world have been focused over the last three years to slow down its spread. A screening scale for the early detection of COVID-19 cases is highly important to diagnose patients before they transmit the disease to others. Using the data collected from Sept. 2020 to Nov. 2020 from 12,123 employees of a company including a weekly self-report questionnaire and information on their demographics, chronic disease, work, and commute, we developed a short, optimized screening scale for the early detection of COVID-19. To develop the scale, we have modified an interpretable machine learning (ML) method which uses integer programming techniques, and consequently reduced its computational cost significantly and made it robust to erroneous data and protective of sensitive information without loss of performance. The developed scale is highly interpretable and easily scored to the extent that it does not require a computer or even a calculator. However, we showed that the operating characteristics of the screening scale on the test dataset are excellent (area under the ROC curve = 82% and area under the Precision–Recall curve = 27%) and comparable to other well-known ML models such as penalized logistic regression and random forest when predicting positive PCR test results within 7 days from the date the self-report questionnaire was filled out. From Dec. 2020 to Nov. 2021, the developed scale has been used in the company weekly to monitor and assess its employees. Although different variants of COVID-19 have emerged since the development of the scale, the results show that the scale has performed well in the early detection of COVID-19, which helped the company take appropriate actions to slow down the spread of COVID-19 among its employees successfully. We expect our findings in this paper to help researchers have a better understanding of COVID-19 and control the spread of the disease. © 2023 Elsevier Ltd
Expert Systems with Applications (09574174) 211
This study addresses the problem of relation detection for answering single-relation factoid questions over knowledge bases (KBs). In this kind of questions, the answer is obtained from a single KB fact in the form of subject-predicate-object. Conventional fact extraction methods have two steps: entity linking and relation detection, in which the output of the entity linking is used by the relation detection step to first find candidate relations, and then choose the best relation from candidate relations. Such methods have difficulties with the relation detection if there is an error or ambiguity in the entity linking step. This paper explores the relation detection task without the entity-linking step utilizing the hierarchical structure of relations and an out-of-box POS tagger. As relations are of different levels of abstraction, the proposed solution uses multiple classifiers in pipeline, each of which uses separate BiGRU neural networks fed with questions embedded with one-hot encoding at the character level. Besides, to increase the accuracy of the proposed model and to avoid the need for large amounts of training data, after each word of the question, its POS tag is inserted before feeding the network. The experimental results show that the accuracy of the proposed solution for the direct relation detection is 89.5%. In addition, the proposed solution can be used for the indirect relation detection whose accuracy is 96.3%, which is higher than state-of-the-art relation detection techniques. Finally, the positive effects of using POS tags have been examined. © 2022 Elsevier Ltd
Kazemi, A. ,
Zojaji, Z. ,
Malverdi, M. ,
Mozafari, J. ,
Ebrahimi, F. ,
Abadani, N. ,
Varasteh, M.R. ,
Nematbakhsh, M.A. Information Retrieval Journal (13864564) 26(1)
Nowadays, a considerable volume of news articles is produced daily by news agencies worldwide. Since there is an extensive volume of news on the web, finding exact answers to the users’ questions is not a straightforward task. Developing Question Answering (QA) systems for the news articles can tackle this challenge. Due to the lack of studies on Persian QA systems and the importance and wild applications of QA systems in the news domain, this research aims to design and implement a QA system for the Persian news articles. This is the first attempt to develop a Persian QA system in the news domain to our best knowledge. We first create FarsQuAD: a Persian QA dataset for the news domain. We analyze the type and complexity of the users’ questions about the Persian news. The results show that What and Who questions have the most and Why and Which questions have the least occurrences in the Persian news domain. The results also indicate that the users usually raise complex questions about the Persian news. Then we develop FarsNewsQA: a QA system for answering questions about Persian news. We developed three models of the FarsNewsQA using BERT, ParsBERT, and ALBERT. The best version of the FarsNewsQA offers an F1 score of 75.61%, which is comparable with that of QA system on the English SQuAD dataset made by the Stanford university, and shows the new Bert-based technologies works well for Persian news QA systems. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Expert Systems with Applications (09574174) 229
Question Classification is the very first and pivotal step in Question Answering(QA) systems. It maps a given question into a predefined class. Classification of Consumer Health Questions contributes to answering medical queries of non-expert users. It plays a principal role in the performance of Consumer Health Question Answering systems. There are several approaches to the classification of questions. However, they do not consider the nature of Consumer Health Questions. In this study, we perform experiments focusing on the question representation methods to classify Consumer Health Questions accurately. These experiments provide an intuition of practical considerations for Consumer Health Question classification. The questions were represented through certain manually-designed features, word embedding, sentence embedding, and finetuning-based techniques. We create question classifiers that involve designing models for representing and categorizing questions. Based on the result, the fine-tuned Bidirectional Encoder Representations from Transformers(BERT)-large-based model with an accuracy of 86.00% on Genetic and Rare Diseases (GARD) and 80.40% on Yahoo! Answers-based datasets outperform previous works. In addition, the method based on A Robustly Optimized BERT Pretraining Approach(RoBERTa)-large model with 86.20% accuracy surpasses fine-tuned BERT-large model on the GARD dataset. Examining the outcomes of the models gives insight into decisive considerations for representing and classifying Consumer Health Questions. © 2023 Elsevier Ltd
Computational Intelligence and Neuroscience (discontinued) (16875273) 2022
Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66% on PASD, 8.42% on PerCQA, and 3.08% on WikiFA datasets in terms of MRR. © 2022 Jamshid Mozafari et al.
IEEE Access (21693536) 10pp. 26045-26057
Developing Question Answering systems (QA) is one of the main goals in Artificial Intelligence. With the advent of Deep Learning (DL) techniques, QA systems have witnessed significant advances. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the QA task; most of them are exclusively in English. In order to address the need for a high-quality QA dataset in the Persian language, we present PersianQuAD, the native QA dataset for the Persian language. We create PersianQuAD in four steps: 1) Wikipedia article selection, 2) question-answer collection, 3) three-candidates test set preparation, and 4) Data Quality Monitoring. PersianQuAD consists of approximately 20,000 questions and answers made by native annotators on a set of Persian Wikipedia articles. The answer to each question is a segment of the corresponding article text. To better understand PersianQuAD and ensure its representativeness, we analyze PersianQuAD and show it contains questions of varying types and difficulties. We also present three versions of a deep learning-based QA system trained with PersianQuAD. Our best system achieves an F1 score of 82.97% which is comparable to that of QA systems on English SQuAD, made by the Stanford University. This shows that PersianQuAD performs well for training deep-learning-based QA systems. Human performance on PersianQuAD is significantly better (96.49%), demonstrating that PersianQuAD is challenging enough and there is still plenty of room for future improvement. PersianQuAD and all QA models implemented in this paper are freely available. © 2013 IEEE.
Journal of Supercomputing (15730484) 78(1)pp. 596-615
Today, social networks have created a wide variety of relationships between users. Friendships on Facebook and trust in the Epinions network are examples of these relationships. Most social media research has often focused on positive interpersonal relationships, such as friendships. However, in many real-world applications, there are also networks of negative relationships whose communication between users is either distrustful or hostile in nature. Such networks are called signed networks. In this work, sign prediction is made based on existing links between nodes. However, in real signed networks, links between nodes are usually sparse and sometimes absent. Therefore, existing methods are not appropriate to address the challenges of accurate sign prediction. To address the sparsity problem, this work aims to propose a method to predict the sign of positive and negative links based on clustering and collaborative filtering methods. Network clustering is done in such a way that the number of negative links between the clusters and the number of positive links within the clusters are as large as possible. As a result, the clusters are as close as possible to social balance. The main contribution of this work is using clustering and collaborative filtering methods, as well as proposing a new similarity criterion, to overcome the data sparseness problem and predict the unknown sign of links. Evaluations on the Epinions network have shown that the prediction accuracy of the proposed method has improved by 8% compared to previous studies. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Dialogue state tracking is one of the main components in task-oriented dialogue systems whose duty is to track the user's goal during the conversation. Due to the diversity in natural languages and existing utterances, the user requests may include unknown values at different turns in these systems. However, predicting the actual values of the user requests is necessary for completing the intended task. In existing studies, these values are determined using span-based methods to predict a span in utterances or previous dialogues. However, the slots are not correctly filled when values are multi-word. In addition, in some scenarios, the slot values in a given turn may depend on previous dialogue states. However, due to the limitation of the input length of language models, it is impossible to access all the previous dialogue states. This study proposes a new approach that uses a span-tokenizer and adds the Bi-LSTM layer on top of the BERT model to predict the exact span of multi-word values. This approach uses parameters like user utterances, important dialogue histories, and all dialogue states as input to decrease the length of the sequences. The results show that this strategy has led to a 1.80% improvement in the joint-goal accuracy and 0.15% improvement in the slot accuracy metrics over the MultiWOZ 2.1 dataset compared to the SAVN model. © 2022 IEEE.
Knowledge-Based Systems (09507051) 235
One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. To overcome these problems, in this paper, we propose a novel sequential word parsing-based method to construct and refine an uncertain question graph that is disambiguated directly over the knowledge graph. Instead of relying on the syntactic dependency relations and some predefined rules that recognize the relations and their arguments, we consider the identified entities and variables in the NLQ as well as their corresponding place in the structure of a query graph pattern to build question triples. First, by leveraging the ordered dependency tree of an NLQ, sentence words are reordered. Then the question graph structure is constructed by parsing the new sequence backward, starting from the identified items. Subsequently, the question graph is refined by eliminating the useless elements. Additionally, to improve the relation similarity measure in the graph similarity process, we exploit the knowledge hidden in a relation pattern taxonomy. Experimental studies over several benchmarks demonstrate that our proposed approach is effective as it achieves promising results in answering the complex NLQs. © 2021
Expert Systems with Applications (09574174) 177
Ontology lexicalization aims to provide information about how the elements of an ontology are verbalized in a given language. Most ontology lexicalization techniques require labeled training data, which are usually generated automatically using the distant supervision technique. This technique is based upon the assumption that if a sentence contains two entities of a triple in a knowledge base, it expresses the relation stated in that triple. This assumption is very simplistic and would lead to generating wrong mappings between sentences and knowledge base triples. In other words, a sentence may contain two entities of a triple, but the relation of entities in the sentence differs from the relation of the triple. Such wrong mappings cause to generating wrong ontology lexicon entries. In this paper, a new method, called denoising distant supervision, is presented to reduce the wrong mappings between sentences and triples by taking the semantic similarity between sentences and the label of triples’ predicate into account. For this purpose, different semantic similarity measures are proposed, which use pre-trained word embeddings to calculate the semantic similarity between the sentences and the label of the triples relation. Then, the sentences whose semantic similarity is low are removed from the mapping. The proposed solution is evaluated in the M−ATOLL framework. The experimental results show that the quality of the generated ontology lexicon under the proposed solution is improved compared to state-of-the-art techniques. © 2021
Recent advances in the field of Question Answering (QA) have improved state-of-the-art results. Due to the availability of rich English training datasets for this task, most results reported are for this language. However, due to the lack of Persian datasets, less research has been done for the latter language therefore the results are hard to compare. In the present work, we introduce the Persian Question Answering Dataset (ParSQuAD) translated from the well-known SQuAD 2.0 dataset. Our dataset comes in two versions depending on whether it has been manually or automatically corrected. The result is the first large-scale QA training resource for Persian. We train three baseline models, one of which, achieves an F1 score of 56.66% and an exact match ratio of 52.86% on the test set with the first version and an F1 score of 70.84 % and an exact match ratio of 67.73% with the second version. © 2021 IEEE.
Distributed and Parallel Databases (09268782) 38(1)
Relations among data entities in most big data sets can be modeled by a big graph. Implementation and execution of algorithms related to the structure of big graphs is very important in different fields. Because of the inherently high volume of big graphs, their calculations should be performed in a distributed manner. Some distributed systems based on vertex-centric model have been introduced for big graph calculations in recent years. The performance of these systems in terms of run time depends on the partitioning and distribution of the graph. Therefore, the graph partitioning is a major concern in this field. This paper concentrates on big graph partitioning approaches for distribution of graphs in vertex-centric systems. This briefly discusses vertex-centric systems and formulates different models of graph partitioning problem. Then, a review of recent methods of big graph partitioning for these systems is shown. Most recent methods of big graph partitioning for vertex centric systems can be categorized into three classes: (i) stream-based methods that see vertices or edges of the graph in a stream and partition them, (ii) distributed methods that partition vertices or edges in a distributed manner, and (iii) dynamic methods that change partitions during the execution of algorithms to obtain better performance. This study compares the properties of different approaches in each class and briefly reviews methods that are not in these categories. This comparison indicates that The streaming methods are good choices for initial load of the graph in Vertex-centric systems. The distributed and dynamic methods are appropriate for long-running applications. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Expert Systems with Applications (09574174) 146
As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (Q/A) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel data-driven graph similarity framework for RDF Q/A to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for same-distance graph elements and equip the process with an edge association-aware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms state-of-the-art methods significantly. © 2020
Turkish Journal Of Electrical Engineering And Computer Sciences (13000632) 28(3)pp. 1474-1490
RDF-based question answering systems give users the capability of natural language querying over RDF data. In order to respond to natural language questions, it is necessary that the main concept of the question be interpreted correctly, and then it is mapped to RDF data. A natural language question includes entities, classes, and implicit and explicit relationships. In this article, by focusing on identification and mapping of implicit relationships in the question (in addition to the explicit relationships), the mapping step has been improved. In the proposed solution (IRQA), entities and implicit/explicit relationships are identified by means of the provided rules and then will be presented as a graph. In the next phase, according to the determined priority of graph nodes, unknown nodes and their adjacent relationships are mapped to their peers in the RDF dataset. The proposed solution is evaluated on the QALD-3 test set. The results prove that the proposed work has improved performance in mapping implicit relationships and reveals higher precision and F-measure values. © 2020 Turkiye Klinikleri. All rights reserved.
ETRI Journal (12256463) 42(2)pp. 239-246
Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results. © 2020 ETRI
In recent years, question and answer systems and information retrieval have been widely used by web users. The purpose of these systems is to find answers to users' questions. These systems consist of several components that the most essential of which is the Answer Selection, which finds the most relevant answer. In related works, the proposed models used lexical features to measure the similarity of sentences, but in recent works, the line of research has changed. They used deep neural networks. In the deep neural networks, early, recurrent neural networks were used due to the sequencing structure of the text, but in state of the art works, convolutional neural networks are used. We represent a new method based on deep neural network algorithms in this research. This method attempts to find the correct answer to a given question from the pool of responses. Our proposed method uses wide convolution instead of narrow convolution, concatenates sparse features vector into feature vector and uses dropout in order to rank candidate answers of the user's question semantically. The results show a 1.01% improvement at the MAP and a 0.2% improvement at the MRR metrics than the best previous model. The experiments show using context-sensitive interactions between input sentences is useful for finding the best answer. © 2019 IEEE.
Knowledge and Information Systems (02191377) 61(2)pp. 847-871
Relations among data items can be modeled with graphs in most of big data sets such as social networks’ data. This modeling creates big graphs with many vertices and edges. Balanced k-way graph partitioning is a common problem with big graphs. It has many applications in several fields. There are many approximate solutions for this problem; however, most of them do not have enough scalability for big graph partitioning and cannot be executed in a distributed manner. Vertex-centric model has been introduced recently as a scalable distributed processing method for big graphs. There are a few methods for graph partitioning based on this model. Existing approaches only consider one-step neighbors of vertices for graph partitioning and do not consider neighbors with higher steps. In this paper, a distributed method is introduced based on vertex-centric model for balanced k-way graph partitioning. This method applies the personalized PageRank vectors of vertices and partitions to decide how vertices are joined partitions. This method has been implemented in the Giraph system. The proposed method has been evaluated with several synthetic and real graphs. Experimental results have shown that this method has scalability for partitioning big graphs. It was also found that this method produces partitions with higher quality compared to the state-of-the-art stream-based methods and distributed methods based on vertex-centric programming model. Its result is close to the results of Metis method. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Expert Systems with Applications (09574174) 95pp. 312-323
Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent. © 2017 Elsevier Ltd
Physica A: Statistical Mechanics and its Applications (03784371) 486pp. 517-534
Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors’ role for each user, a two-level method is proposed for both detecting users’ influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects. © 2017 Elsevier B.V.
Journal of Information Science (01655515) 43(3)pp. 412-423
Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes. © Chartered Institute of Library and Information Professionals.
Reordering is one of the most important factors affecting the quality of the output in statistical machine translation (SMT). A considerable number of approaches that proposed addressing the reordering problem are discriminative reordering models (DRM). The core component of the DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately, the relationship between classification quality and ultimate SMT performance has not been investigated to date. Understanding this relationship will allow researchers to select the classifier that results in the best possible MT quality. It might be assumed that there is a monotonic relationship between classification quality and SMT performance, i.e., any improvement in classification performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally show that this assumption does not always hold, i.e., an improvement in classification performance might actually degrade the quality of an SMT system, from the point of view of MT automatic evaluation metrics. However, we show that if the improvement in the classification performance is high enough, we can expect the SMT quality to improve as well. In addition to this, we show that there is a negative relationship between classification accuracy and SMT performance in imbalanced parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy, the metric commonly used to date. © 2017 by the authors.
Expert Systems with Applications (09574174) 84pp. 186-199
We present a syntax-based reordering model (RM) for hierarchical phrase-based statistical machine translation (HPB-SMT) enriched with semantic features. Our model brings a number of novel contributions: (i) while the previous dependency-based RM is limited to the reordering of head and dependant constituent pairs, we also model the reordering of pairs of dependants; (ii) Our model is enriched with semantic features (Wordnet synsets) in order to allow the reordering model to generalize to pairs not seen in training but with equivalent meaning. (iii) We evaluate our model on two language directions: English-to-Farsi and English-to-Turkish. These language pairs are particularly challenging due to the free word order, rich morphology and lack of resources of the target languages. We evaluate our RM both intrinsically (accuracy of the RM classifier) and extrinsically (MT). Our best configuration outperforms the baseline classifier by 5–29% on pairs of dependants and by 12–30% on head and dependant pairs while the improvement on MT ranges between 1.6% and 5.5% relative in terms of BLEU depending on language pair and domain. We also analyze the value of the feature weights to obtain further insights on the impact of the reordering-related features in the HPB-SMT model. We observe that the features of our RM are assigned significant weights and that our features are complementary to the reordering feature included by default in the HPB-SMT model. © 2017 Elsevier Ltd
Expert Systems with Applications (09574174) 42(2)pp. 913-928
The profiling of background knowledge is essential in scholar's recommender systems. Existing ontology-based profiling approaches employ a pre-built reference ontology as a backbone structure for representing the scholar's preferences. However, such singular reference ontologies lack sufficient ontological concepts and are unable to represent the hierarchical structure of scholars' knowledge. They rather encompass general-purpose topics of the domain and are inaccurate in representing the scholars' knowledge. This paper proposes a method for integrating of multiple domain taxonomies to build a reference ontology, and exploits this reference ontology for profiling scholars' background knowledge. In our approach, various topics of Computer Science domain from Web taxonomies are selected, transformed by DBpedia, and merged to construct a reference ontology. We demonstrate the effectiveness of our approach by measuring five quality-based metrics as well as application-based evaluation against the developed reference ontology. The empirical results show an improvement over the existing reference ontologies in terms of completeness, richness, and coverage. © 2014 Elsevier Ltd. All rights reserved.
Expert Systems with Applications (09574174) 42(6)pp. 3268-3295
In a multi-attribute combinatorial double auction (MACDA), sellers and buyers' preferences over multiple synergetic goods are best satisfied. In recent studies in MACDA, it is typically assumed that bidders must know the desired combination (and quantity) of items and the bundle price. They do not address a package combination which is the most desirable to a bidder. This study presents a new packaging model called multi-attribute combinatorial bidding (MACBID) strategy and it is used for an agent in either sellers or buyers side of MACDA. To find the combination (and quantities) of the items and the total price which best satisfy the bidder's need, the model considers bidder's personality, multi-unit trading item set, and preferences as well as market situation. The proposed strategy is an extension to Markowitz Modern Portfolio Theory (MPT) and Five Factor Model (FFM) of Personality. We use mkNN learning algorithm and Multi-Attribute Utility Theory (MAUT) to devise a personality-based multi-attribute combinatorial bid. A test-bed (MACDATS) is developed for evaluating MACBID. This test suite provides algorithms for generating stereotypical artificial market data as well as personality, preferences and item sets of bidders. Simulation results show that the success probability of the MACBID's proposed bundle for selling and buying item sets are on average 50% higher and error in valuation of package attributes is 5% lower than other strategies. © 2014 Elsevier Ltd. All rights reserved.
Computers and Electrical Engineering (00457906) 44pp. 218-240
Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to cloud. Resource allocation is one of the most challenging issues in MCC which is investigated in this paper considering neighboring mobile devices as service providers. The objective of the resource allocation is to select service providers minimizing the completion time of the offloading along maximizing lifetime of mobile devices satisfying deadline constraint. The paper proposes a two-stage approach to solve the problem: first, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to obtain the Pareto solution set; second, entropy weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are employed to specify the best compromise solution. Furthermore, a context-aware offloading middleware is developed to collect contextual information and handle offloading process. Moreover, to stimulate selfish users, a virtual credit based incentive mechanism is exploited in offloading decision. The experimental results demonstrate the ability of the proposed resource allocation approach to manage the trade-off between time and energy comparing to traditional algorithms. © 2015 Elsevier Ltd. All rights reserved.
We propose a novel dependency-based reordering model for hierarchical SMT that predicts the translation order of two types of pairs of constituents of the source tree: head-dependent and dependent-dependent. Our model uses the dependency structure of the source sentence to capture the medium- and long-distance reorderings between these pairs of constituents. We describe our reordering model in detail and then apply it to a language pair in which the languages involved follow different word order patterns, English (SVO) and Farsi (free word order being SOV the most frequent pattern). Our model outperforms a baseline (standard hierarchical SMT) by 0.78 BLEU points absolute, statistically significant at p = 0.01. © 2015 The authors.
Expert Systems with Applications (09574174) 42(7)pp. 3801-3812
Memory-based collaborating filtering techniques are widely used in recommender systems. They are based on full initial ratings in a user-item matrix. However, most of the time in group recommender systems, this matrix is sparse and users' preferences are unknown. This deficiency may make memory-based collaborative filtering unsuitable for group recommender systems. This paper, improves memory-based techniques for group recommendation systems by resolving the data sparsity problem. The core of the proposed method is based on a support vector machine learning model that computes similarities between items. This method employs calculated similarities and enhances basic memory-based techniques. Experiments demonstrate that the proposed method overcomes the memory-based techniques. It also indicates that the presented work outperforms the latent factor approach, which is very efficient in sparse conditions. Finally, it is indicated that the proposed method gives a better performance than existing approaches on generating group recommendations. © 2014 Elsevier Ltd.
Physica A: Statistical Mechanics and its Applications (03784371) 436pp. 833-845
Recently an increasing amount of research is devoted to the question of how the most influential nodes (seeds) can be found effectively in a complex network. There are a number of measures proposed for this purpose, for instance, high-degree centrality measure reflects the importance of the network topology and has a reasonable runtime performance to find a set of nodes with highest degree, but they do not have a satisfactory dissemination potentiality in the network due to having many common neighbors (CN(1)) and common neighbors of neighbors (CN(2)). This flaw holds in other measures as well. In this paper, we compare high-degree centrality measure with other well-known measures using ten datasets in order to find a proportion for the common seeds in the seed sets obtained by them. We, thereof, propose an improved high-degree centrality measure (named DegreeDistance) and improve it to enhance accuracy in two phases, FIDD and SIDD, by put a threshold on the number of common neighbors of already-selected seed nodes and a non-seed node which is under investigation to be selected as a seed as well as considering the influence score of seed nodes directly or through their common neighbors over the non-seed node. To evaluate the accuracy and runtime performance of DegreeDistance, FIDD, and SIDD, they are applied to eight large-scale networks and it finally turns out that SIDD dramatically outperforms other well-known measures and evinces comparatively more accurate performance in identifying the most influential nodes. © 2015 Published by Elsevier B.V.
Communications in Computer and Information Science (18650937) 428pp. 145-154
Due to resource scarcity of mobile devices in Mobile Cloud Computing (MCC), intensive computing applications are offloaded into the cloud. There is a three-tier architecture for MCC consisting of distant cloud servers, nearby cloudlets and adjacent mobile devices. In this paper, we consider third tier. We propose an Optimal Fair Multi-criteria Resource Allocation (OFMRA) algorithm that minimizes the completion time of offloading applications along maximizing lifetime of mobile devices. Furthermore to stimulate selfish devices to participate in offloading, a virtual price based incentive mechanism is presented. The paper also designs an Offloading Mobile Cloud Framework (OMCF) which collects profile information and handles the offloading process. A prototype of the proposed method has been implemented and evaluated using a high computational load application. The results show that the proposed algorithm manages the tradeoff between optimizing completion time and energy well and improves the performance of offloading using the incentive mechanism. © Springer International Publishing Switzerland 2014.
Frontiers in Artificial Intelligence and Applications (09226389) 265pp. 1058-1072
Collecting precise knowledge from scholars' context for profiling is crucial in recommender systems as profiles provide foundational information for successful recommendation. However, acquiring of scholars' knowledge is often a challenging task since it is associated with difficulties including: what are the appropriate knowledge resources, how knowledge items can be unobtrusively captured, and how heterogeneity among different knowledge sources should be resolved. Despite the availability of various knowledge resources, identification and collecting comprehensive knowledge in an unobtrusive manner is not straightforward. To address these issues, we analyze the scholar academic behaviors and collect various scholars' knowledge diffused over the Web. The result of empirical evaluation shows the efficiency of our approach in terms of completeness and accuracy. © 2014 The authors and IOS Press. All rights reserved.
International Journal of Pattern Recognition and Artificial Intelligence (02180014) 28(2)
The rdfs:seeAlso predicate plays an important role in linking web resources in semantic web. Based on the W3C definition, it shows that the object resource provide additional information about the subject resource. Since providing additional information can take various forms, the definition is generic. In the other words, the rdfs:seeAlso link can present different meanings to the users and it can represents different kind of patterns and relationships between web resources. These patterns are unknown and have to be specified to help organizations, and individuals to interlink, and publish their datasets on Web of Data using the rdfs:seeAlso link. In this paper, we investigate to the traditional usages of seealso and then present a methodology to specify the patterns of rdfs:seeAlso usages in Semantic Web. The results of our investigation show that the discovered patterns constitute a significant portion of rdfs:seeAlso usages in Web of Data. © 2014 World Scientific Publishing Company.
CEUR Workshop Proceedings (16130073) 1210
Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group satisfaction and lower group dissatisfaction.
RDF is a data model to represent structured data on the web. SPARQL is a query language for RDF data that returns exactly matching results. Number of these results may be very high. By rapid growth of web of data the need for efficient ranking methods for results of this kind of queries is increased. Because of exactly matching results in SPARQL queries, the focus is on the query independent features for ranking them. We use a learning to rank approach with four sets of query independent features to rank entity results of SPARQL queries over DBpedia. These features include: features extracted from RDF graph, weighted LinkCount, search engine based and information content of the RDF resource. We investigate the performance of individual features and the combination of them in learning to rank entity results. Experiments show that the complete feature set has the best performance in rankings. As an individual feature, the proposed information content of the RDF resource is a good choice based on its performance in ranking and the elapsed time for extracting this feature. © 2014 IEEE.
Linked Data is used in the Web to create typed links between data from different sources. Connecting diffused data by using these links provides new data which could be employed in different applications. Association Rules Mining (ARM) is a data mining technique which aims to find interesting patterns and rules from a large set of data. In this paper, the problem of applying association rules mining using Linked Data in centralization approach has been addressed i.e. arranging collected data from different data sources into a single dataset and then apply ARM on the generated dataset. Firstly, a number of challenges in collecting data from Linked Data have been presented, followed by applying the ARM using the dataset of connected data sources. Preliminary experiments have been performed on this semantic data showing promising results and proving the efficiency, robust, and useful of the used approach. © 2013 IEEE.
International Journal of Web Engineering and Technology (17419212) 8(4)pp. 395-411
In the web of data, linked datasets are changed over time. These changes include updating on features and address of entities. The address change in RDF entities causes their corresponding links to be broken. Broken link is one of the major obstacles that the web of data is facing. Most approaches to solve this problem attempt to fix broken links at the destination point. These approaches have two major problems: a single point of failure; and reliance on the destination data source. In this paper, we introduce a method for fixing broken links which is based on the source point of links, and discover the new address of the detached entity. To this end, we introduce two datasets, which we call 'superior' and 'inferior'. Through these datasets, our method creates an exclusive graph structure for each entity that needs to be observed over time. This graph is used to identify and discover the new address of the detached entity. Afterward, the most similar entity, which is candidate for the detached entity, is deduced and suggested by the algorithm. The proposed model is evaluated with DBpedia dataset within the domain of 'person' entities. The result shows that most of the broken links, which had referred to a 'person' entity in DBpedia, had been fixed correctly. Copyright © 2013 Inderscience Enterprises Ltd.
Journal of Information Science (01655515) 39(2)pp. 198-210
Similarity estimation between interconnected objects appears in many real-world applications and many domain-related measures have been proposed. This work proposes a new perspective on specifying the similarity between resources in linked data, and in general for vertices of a directed graph. More specifically, we compute a measure that says 'two objects are similar if they are connected by multiple small-length shortest path'. This general similarity measure, called SRank, is based on simple and intuitive shortest paths. For a given domain, SRank can be combined with other domain-specific similarity measures. The suggested model is evaluated in a clustering procedure on a sample data from DBPedia knowledge-base, where the class label of each resource is estimated and compared with the ground-truth class label. Experimental results show that SRank outperforms other similarity measures in terms of precision and recall rate. © The Author(s) 2012.
Journal Of Universal Computer Science (0948695X) 19(13)pp. 1871-1891
This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation in Web of Data and shows the role of designed system in interlinking datasets in Web of Data and word sense disambiguation scope. We specify the core labelling properties in semantic web to identify the name of entities which are described in web resources and use them to identify the candidate senses for a web resource. Moreover, we define the web resource's context to identify the most appropriate sense for each of the input web resources. Evaluation of the system shows the high coverage of the core labelling properties and the high performance of the sense disambiguation algorithm. © J.UCS.
ETRI Journal (12256463) 34(5)pp. 743-752
Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Naïve Bayes, and a decision tree, is also shown. © 2012 ETRI.
Journal of Theoretical and Applied Electronic Commerce Research (07181876) 7(3)pp. 1-10
With the advent of mobile networks and mobile devices, mobile payment has been attractive to many ecommerce users. Mobile environment features a wide range and an increasing number of access devices and network technologies. Context-aware content/service adaptation is deemed necessary to ensure best user experience. We developed an Adaptation Management Framework (AMF) Mobile Payment Service which manages the complexity of dynamic and autonomous mobile payment service. In this Framework, a personal assistant agent is presented for automatic and intelligent payment and a model is implemented for inference and making appropriate decisions. To evaluate the suggested framework, context data were collected from content providers and a mobile network operator. The obtained results confirm the efficiency of the method. © 2012 Universidad de Talca - Chile.
Modern Applied Science (discontinued) (19131852) 6(10)pp. 37-52
Most of the ontology alignment tools use terminological techniques as the initial step and then apply the structural techniques to refine the results. Since each terminological similarity measure considers some features of similarity, ontology alignment systems require exploiting different measures. While a great deal of effort has been devoted to developing various terminological similarity measures and also developing various ontology alignment systems, little attention has been paid to develop similarity search algorithms which exploit different similarity measures in order to gain benefits and avoid limitations. We propose a novel terminological search algorithm which tries to find an entity similar to an input search string in a given ontology. This algorithm extends the search string by creating a matrix from its synonym and hypernyms. The algorithm employs and combines different kind of similarity measures in different situations to achieve a higher performance, accuracy, and stability in comparison with previous methods which either use one measure or combine more measures in a naive ways such as averaging. We evaluated the algorithm using a subset of OAEI Bench mark data set. Results showed the superiority of proposed algorithm and effectiveness of different applied techniques such as word sense disambiguation and semantic filtering mechanism.
With the growing amount of published RDF datasets on similar domains, data conflict between similar entities (same-as) is becoming a common problem for Web of Data applications. In this paper we propose an algorithm to detect conflict of same properties values of similar entities and select the most accurate value. The proposed algorithm contains two major steps. The first step filters out low ranked datasets using a link analysis technique. The second step calculates and evaluates the focus level of a dataset in a specific domain. Finally, the value of the top ranked dataset is considered. The proposed algorithm is implemented by Java Programming Language and is evaluated by geographical datasets containing 'country' entities. © 2012 IEEE.
International Journal of Electronic Marketing and Retailing (17411025) 5(2)pp. 128-146
Preventing customer churn and trying to retain customers is the main object of customer churn management. This paper proposes a model to measure churn probability and introduces a policy to retain customers. Using existing datasets of customers, we calculated CLV and used the C5.0 technique to predict churn probability for each customer. We also used process mining to find a policy to retain each customer separately. The model was simulated using a super market chain (Refah) and the results show the model is performing much better than previous proposed models. A computer result is shown. Copyright © 2012 Inderscience Enterprises Ltd.
Modern Applied Science (discontinued) (19131852) 6(9)pp. 42-55
Web service technology (WST) is a service-oriented architecture implementation framework that makes designing component-based internet applications possible. At present, many providers offer their services as web services. Current WST suffers from the lack of an integrated tool to assist web service developers. In WST, the services are published publicly, and their descriptions are stored in service directories. These descriptions contain valuable information about the work of different software teams throughout the world. However, with the increasing number of web services, searching for services is difficult and time-consuming. Furthermore, in current service directories, there is a little knowledge about the services, and extraction of useful information to be utilised by developers is not easy. In this paper, in order to increase the knowledge of what is available in service directories, a structure is presented by interlinking WST entities by using some defined semantic relations. The proposed structure provides a framework and a tool named WSDATool to develop new web services using information from published services or to refine current published web service descriptions. In experiments, services designed with the assistance of the WSDATool are more coherent and well designed.
Combinatorial auctions are auctions in which bidders bid on combinations of items, bundles, instead of on individual items. In these auctions, bidders always tend to construct and bid on the most beneficial bundles of items, while facing a substantial number of items. Since there are a huge number of items available in a combinatorial auction, deciding on which items to put in bundles is a challenge for bidders. In combinatorial auctions, bundling of items and bidding on the best possible bundles are of great importance and developing an efficient bidding strategy can increase quality of the auctions considerably. In this paper, we have proposed an efficient bidding strategy. Performance of the proposed strategy in various markets has been simulated and compared with the bidding strategies already available in the literature. The obtained results show that in comparison with the previously available bidding strategies, the proposed strategy is more beneficial to both bidders and auctioneer, especially in markets where there is a considerable difference between values of items. © 2011 IEEE.
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which employs user context and semantic concepts to discover new words for obtaining accurate results. Experimental results show an enhancement on information retrieval performance comparing to the traditional approaches. © 2011 IEEE.
ADVANCES IN ARTIFICIAL INTELLIGENCE (03029743) 6657pp. 301-312
In this paper we have proposed a context-aware reputation-based trust model for multi-agent environments. Due to the lack of a general method for recognition and representation of context notion, we proposed a functional ontology of context for evaluating trust (FOCET) as the building block of our model. In addition, a computational reputation-based trust model based on this ontology is developed. Our model benefits from powerful reasoning facilities and the capability of adjusting the effect of context on trust assessment. Simulation results shows that an appropriate context weight results in the enhancement of the total profit in open systems.
Web services are increasingly used to integrate and build business application on the internet. Failure of web services is not acceptable in many situations such as online banking, so fault tolerance is a key challenge of web services. Web service architecture still lacks facilities to support fault tolerance. This paper proposes a fault tolerant architecture for web services by increasing the reliability and availability, the architecture is based on application-level and transport-level logging of requests and replies, N-Version and active replication techniques. The proposed architecture is client transparent and provides fault tolerance even for requests being processed at the time of server failure. © 2011 IEEE.
In the MAC protocols based on the S-MAC scheme, usually the combination of periodic sleep/listen scheduling and four-way handshake mechanism is employed to reduce idle listening and avoid interference. However, this combination greatly degrades network capacity and results in high end-to-end latency. In this paper, we propose Adaptive IAMAC to increase channel utilization and improve communication efficiency, specifically in large-scale sensor networks with low duty cycle. Adaptive IAMAC allows multiple nodes to transmit to their common parent during a frame. Moreover, it includes the adaptive parent selection mechanism, which enables the nodes to change their parent according to the currently overheard control packets at the MAC layer. Through these techniques, Adaptive IAMAC enhances network throughput, reduces end-to-end latency, and moderates the overhead of four-way handshake mechanism. Simulation results confirm that Adaptive IAMAC provides significant improvements over S-MAC in terms of throughput, latency, and energy efficiency. © 2011 IEEE.
Aslib Proceedings (0001253X) 63(6)pp. 555-569
Purpose – The aim of this paper is to investigate contextual information that has an impact on the process of selection and decision making in recommender systems (RSs) in digital libraries. Design/methodology/approach – Using a grounded theory method of qualitative research, semistructured interviews were carried out with 22 information specialists, and IT and computer engineering students and professors. Data resulting from interviews were analysed in two stages using open coding, followed by axial and selective coding. Findings – The central idea (concept) developed in this study, named scientific research ground (SRG), is an information ground users step into with scholarly purposes. Within SRG they start interacting with information systems. SRG has contexts which situate users in a range of situations while interacting with information systems. Users' characteristics such as purpose, activity, literacy, mental state, expectations, and assumptions, occupational and social status are some contexts that should be taken into account for making a recommendation. Research limitations/implications – This study sought to explore contextual information in the academic community and the academic contextual information cannot be generalized to RSs in other environments such as ecommerce. Practical implications – Identifying and implementing contextual information in information systems can help make better recommendations as well as improve interaction between users and information systems. Originality/value – Based on the SRG idea and its contexts, a multilayer contextual model for a recommender system is proposed. © 2011, Emerald Group Publishing Limited
Roozmand, O. ,
Ghasem-aghaee, N. ,
Hofstede, G.J. ,
Nematbakhsh, M.A. ,
Baraani, A. ,
Verwaart, T. Knowledge-Based Systems (09507051) 24(7)pp. 1075-1095
Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede's model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae's five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. © 2011 Elsevier B.V. All rights reserved.
International Journal Of Information Science And Management (20088310) 9(2)pp. 1-12
Several semantic image search schemes have been recently proposed to retrieve images from the web. However, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. In this paper, we make use of context to further confine the outcome of the semantic search engines. For this purpose, we propose a hybrid search engine which utilizes concept and context for retrieving precise results. In the proposed model, an ontology is exploited for annotating images and accomplishing search process in the semantic level. Furthermore, the query of the user is modified with the concepts available in the ontology. Next, we make use of search context of the user and augment the query with the information extracted from the user's context to additionally eliminate irrelevant results. Experimental results show that the combination of concept and context is effective in retrieving and presenting the most relevant results to the user.
Advances in Intelligent and Soft Computing (18675670) 124pp. 41-51
With the advent of mobile networks and mobile devices,mobile payment has been attractive to many ecommerce users. However, due to mobility of users, payment services and mobile devices must be adapted to the new environment. In this paper, a personal assistant agent is presented for automatic and intelligent payment and a model is implemented for inference and making appropriate decisions. To evaluate the suggested framework, context data were collected from content providers and a mobile network operator. The obtained results confirm the efficiency of the method. © 2011 Springer-Verlag Berlin Heidelberg.
The significant feature of a social networking website is the primary reason they are made for: connecting people and friends via internet. "Friend recommender systems" are wisely designed for finding people, most of whom tend to be with the same interests and backgrounds. These systems use a set of predefined items from which users specify their preferences simply by selecting from a fixed list. As a result, they can't put it in their own words. Moreover, these systems only consider the "exact similarity matching" among the users' interests to find and recommend new friends. The main focus of this paper is to introduce a new approach for matching more compatible friends on social networking websites. Contrary to existing approaches, our system let users specify their interests in their own words. Thus, users do not need to select their preferences from a predefined list. In addition, we define "compatibility" by introducing two new relations between users' interests: "semantic" and "complementary" relations for the purpose of matching compatible users. We chose 50 members from LiveJournal social network as our experimental case in this study and calculated compatibility degrees between each pair of them. The results show that the average error of this system is 0.2 which is acceptable in comparison with the similarity matching friend recommendation systems in which the average rate of error is 0.6. © 2011 IEEE.
Nematbakhsh, M.A. ,
Kazemifard M. ,
Zaeri, A. ,
Ghasem-aghaee, N. ,
Mardukhi F. ,
Kazemifard M. ,
Zaeri, A. ,
Ghasem-aghaee, N. ,
Nematbakhsh, M.A. ,
Mardukhi F. Applied Soft Computing (15684946) 11(2)pp. 2260-2270
Software development cost estimation is important for effective project management. Many models have been introduced to predict software development cost. In this paper, a novel emotional COnstructive COst MOdel II (COCOMO II) has been proposed for software cost estimation. In COCOMO II only the project characteristics are considered, whereas the characteristics of team members are also important factors. This paper presents a model, namely FECSCE, which in addition to project characteristics considers the communication skills, personality, mood and capabilities of team members. In FECSCE, we have used a Multi-Agent System (MAS) in order to simulate team communications. © 2010 Elsevier B.V. All rights reserved.
Journal of Intelligent and Fuzzy Systems (18758967) 22(5-6)pp. 217-236
Collaborative and group-based queries aim to find one or more points in a search space which have the minimum aggregate distance to all members of a group, situated at a set of query points. Current approaches like Group Nearest-Neighbor (GNN) queries are based on single-measure models of distance, like Euclidean distance. In reality, human has a multi-measure perception of distance so that spatial, temporal and economical aspects are important to people with possibly different individual preferences. Current approaches to GNN are unable to handle such distance measures, since it depends on the perceptions and preferences of the users. In this study, we focus on the role of users, as members of a group, situated at GNN query points. An enriched model of distance is introduced which takes the advantage of interval type-2 fuzzy sets to cope with high-order distance uncertainties, emerged from different perceptions of distance by users, and their different preferences. The flexibility of this aggregate model in handling uncertainty enables every member of the group to use a set of group-defined words to express his/her perception of multiple distance types, and to use words instead of numeric values to set the weights for each distance type according to his/her preferences. Our experimental evaluations show that the query results are closer to group preferences by providing higher quality of consensus, while keeping the spatial dispersion of the top-k results at a small level, and improved performance with reasonable response time. The proposed distance model also provides more robustness to changes of mobile member locations, eliminating unnecessary repeated computations. © 2011 - IOS Press and the authors. All rights reserved.
Tsinghua Science And Technology (10070214) 16(5)pp. 475-490
The existing multipath routing protocols for wireless sensor networks demonstrate the efficacy of traffic distribution over multiple paths to fulfill the Quality of Service (QoS) requirements of different applications. However, the performance of these protocols is highly affected by the characteristics of the wireless channel and may be even inferior to the performance of single-path approaches. Specifically, when multiple adjacent paths are being used concurrently, the broadcast nature of wireless channels results in interpath interference which significantly degrades end-to-end throughput. In this paper, we propose a Low-Interference Energy-efficient Multipath Routing protocol (LIEMRO) to improve the QoS requirements of event-driven applications. In addition, in order to optimize resource utilization over the established paths, LIEMRO employs a quality-based load balancing algorithm to regulate the amount of traffic injected into the paths. The performance gain of LIEMRO compared to the ETX-based single-path routing protocol is 85, 80, and 25 in terms of data delivery ratio, end-to-end throughput, and network lifetime, respectively. Furthermore, the end-to-end latency is improved more than 60. © 2011 Tsinghua University Press.
Mobile Information Systems (discontinued) (1875905X) 7(2)pp. 123-145
In Group Nearest-Neighbor (GNN) queries, the goal is to find one or more points of interest with minimum sum of distance to the current location of mobile users. The classic forms of GNN use Euclidean distance measure which is not sufficient to capture other essential distance perceptions of human and the inherent uncertainty of it. To overcome this problem, an improved distance model can be used which is based on a richer, closer to real-world type-2 fuzzy logic distance model. However, large search spaces as well as the need for higher-order uncertainty management will increase the response times of such GNN queries. In this paper two fuzzy clustering methods combined with spatial tessellation are exploited to reduce the search space. Extensive evaluation of the proposed method shows improved response times compared to naïve method while maintaining a high quality of approximation. The proposed uncertainty management method also provides robustness to movement of mobile users, eliminating the need for full re-computation of candidate clusters when the locations of group members are changed. © 2011 - IOS Press and the authors. All rights reserved.
International Review on Modelling and Simulations (19749821) 4(6)pp. 3261-3272
One of the existing sources in digital libraries is electronic journals. Librarians use ejournals' usage data for many practical applications (e.g. to begin or end subscriptions). Publishers and vendors, who have these data, usually don't share them with the librarians or represent only that data which support their sales products. One possible method for evaluation is preparing monitoring system and logging the data related to users' interactions with digital library and collecting transactions and using data analyzing techniques. In this research web usage mining method has been used. Since server log files don't contain precise information and details of users' interactions, this research is going to use client side data, special new category of data called Intentional Browsing Data(IBD), for improving quality of discovered knowledge and exploit IBD as a criterion for determining the journals' desirability in students' viewpoint.The obtained knowledge from analyzing discovered patterns can be used to make decision in journal management and it is a new method in this field. Digital library of Islamic Azad University of Najaf Abad (IAUN) is considered as a practical domain of this research. By using obtained patterns of mining and analyzing collected data, a model is presented to identify desirability of journal according to students' viewpoint. © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Keyword based information retrieval has difficulties in retrieving relevant information because it is not able to include the semantics of queries. In this paper, we present a novel method for query expansion based on semantic relations. In our proposed algorithm, semantically related words to the query are extracted from WordNet. Valuable words among extracted words are selected as candidate expansion terms. At last candidate terms which do not cause ambiguity and noise in the query are selected as expansion words. This approach is naturally robust to noise words and can improve semantic inferring of information retrieval. © 2010 IEEE.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities. Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies. In this paper, we will point out the problems and shortcomings of current similarity aggregation strategies and propose a new strategy, which enables us to utilize the structural information of ontologies to get weights of matchers for the similarity aggregation task. We have tested our similarity aggregation strategy on the OAEI 2009 data set. Experimental results show a significant accuracy in several cases, especially for matching the classes of ontologies. © 2010 IEEE.
An important class of community-based activities of mobile users is searching and querying for common locations to visit or come together for a specific task. For this purpose, Group Nearest-Neighbor (GNN) queries are used a generalization of nearest-neighbor queries where the goal is to find one or more points from a set of destination points that have the smallest total distance from all query points. Since users are situated at the query points as members of a group, and the perception of people about distance can be different, the classic GNN models cannot be used such differences. On the other hand, more rich and multi-faceted distance models based on type-2 fuzzy logic exist but they require heavy computations which makes them difficult to use in real-world applications. In this paper, we propose a method based on spatial tessellation and fuzzy clustering of destination points that helps to compute the approximate response to GNN query in efficient time. For this purpose, Voronoi diagrams and two fuzzy clustering methods are compared using several evaluation criteria. The results show that the proposed method provides higher performance while keeping a good quality of approximation in terms of similarity between ideal and approximated response sets. The proposed uncertainty management method also improves the robustness of system to small movements of mobile group members. © 2010 IEEE.
International Review on Computers and Software (discontinued) (18286003) 5(6)pp. 643-651
Group nearest-neighbor (GNN) queries are a generalization of nearest-neighbor queries where the goal is to find one or more points from a set of destination points that have the smallest total distance from all query points. In fact, since people are situated at the query points as members of a group, and the perception of people about distance can be different, the classic GNN models cannot be used. On the other hand, more rich and multi-faceted distance models based on type-2 fuzzy logic require heavy computations which makes them difficult to use in realworld applications. In this paper, we propose a method based on fuzzy clustering of destination points that helps to compute the approximate response to GNN query in efficient time. For this purpose, two fuzzy clustering methods are compared using four evaluation criteria. The results show that one of the fuzzy clustering methods provides a high performance improvement while keeping a good quality of approximation in terms of similarity between ideal and approximated response sets. © 2010 Praise Worthy Prize S.r.l.
International Journal of Human Computer Studies (10959300) 67(1)pp. 1-35
Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains. © 2008 Elsevier Ltd. All rights reserved.
With high increasing documents and electronic texts in Persian language, the use of fast methods to achieve texts through huge sets of documents is highly crucial. Persian text summarization which shows the main concept of a text in minimum size is an effective solution. One of the steps in Persian text summarization is to stem and eliminate common words. The aim of this research is to stem words from Persian documents to make their use more efficient in text summarization, the present method is to eliminate words and stem keywords. The compound of existing techniques in the words network was used to create a Persian database using the Dehkhoda dictionary. The algorithm used for summarization is based on statistical techniques. In this method each sentence is given an important weight, sentences with higher weight are used for summarization. By comparing the results of other algorithms on Persian texts we concluded that our technique extracts the root of the existing words with more precision. ©2009 IEEE.
Expert Systems with Applications (09574174) 36(3 PART 2)pp. 5768-5774
Consumers prefer to purchase bundled and related products to use them together to perform a task or satisfy a need. In this paper, we propose a complementary association for bundling products to enhance promotions, recommendations and selling strategies in marketplaces such as combinatorial auction. We propose an ontology based model and define a Need association to determine complement of product classes. Using this type of association, we develop a mathematical model to relatively measure complementary degree of classes and the latest purchased products to recommend Top-N products. We experiment this approach with a recommender system utilizing complementary products. Experimental results on the dataset of Building Equipment Company show superiority in terms of performance and precision. © 2008 Elsevier Ltd. All rights reserved.
AIP Conference Proceedings (0094243X) 1117pp. 180-188
ACS algorithms have been used in solving NP-hard and optimization problems in recent years. ACS ant colonies are homogeneous, but natural colonies are not. In this paper, a new ACS algorithm is proposed. It uses heterogeneous ant colonies which are evolved using a new type of genetic algorithm. Experimental results obtained from solving TSP problem, show the superiority of proposed algorithm over classical ACS. © 2009 American Institute of Physics.
International Review on Computers and Software (discontinued) (18286003) 4(6)pp. 672-683
In recent years, much effort has been put in finding semantic associations between items. One type of these associations is complementary association between items which determines an item that its usage is interrelated with the use of an associated or paired item such that a demand for one generates demand for the other. This association has many applications in the field of economics and marketing. This paper presents a novel contribution in this area, proposing an automatic and unsupervised method for acquiring complementary associations between products in a product catalog, framed in the context of domain ontology learning, using the Web as corpus. The paper also discusses how obtained associations can be automatically evaluated against WordNet and presents encouraging results for several categories of products. © 2009 Praise Worthy Prize S.r.l. - All rights reserved.
Evaluating trust is a context-aware application that several models have been proposed for that in the literature. However, small numbers of these models are context-aware and each of them has its own specific context definition and representation. Due to the role of reputation as one of the main input factors for evaluating trust and lack of a general method for recognition and representation of the notion of context for reputation; we have proposed a context-aware ontological reputation model for evaluating trust in this paper. The proposed model can be used to build special services on a middleware layer which could be applied to different reputation-based trust evaluation models transparently to make them context-aware. Moreover, CORMET enables the interoperation of reputation-based trust models which use different internal context models.
In this paper, we propose IAMAC, an Interference Avoidance MAC protocol to avoid inter-node interference in dense wireless sensor networks. IAMAC interacts with routing protocol via cross-layer information sharing between the MAC and network layer. By providing information from network layer, we enable the MAC protocol to make proper decisions which result in fewer inter-node interference and lower delay. Through interference avoidance, IAMAC reduces energy consumption per node and leads to higher network lifetime compared with S-MAC and Adaptive S-MAC. In addition, IAMAC has lower delay than S-MAC. In our evaluations, we considered IAMAC in conjunction with two error recovery methods (ARQ and Seda). Our simulation results show that our protocol is highly compatible with Seda and this integration achieves higher network throughput and lifetime. © 2009 IEEE.
Information Sciences (00200255) 179(3)pp. 248-266
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers' profits in promotion and marketing models. In addition to the sellers' profits, three important factors - annoyance of customers, sellers' reputations, and customers' anonymity - are considered in this study. Without considering the customer's profile, we minimize unrelated and disliked offers to reduce the customer's annoyance and elevate the seller's reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information. © 2008 Elsevier Inc. All rights reserved.
Blinded data mining is a branch of data mining technique which is focused on protecting user privacy. To mine sensitive data such as medical information, it is desirable to protect privacy and there is not worry about revealing personalized data. In this paper a new approach for blinded data mining is suggested. It is based on ontology and k-anonymity generalization method. Our method generalizes a private table by considering table fields' ontology, so that each tuple will become k-anonymous and less specific to not reveal sensitive information. This method is implemented using protégé java for evaluation. ©2009 IEEE.
Simulation and Gaming (1552826X) 39(1)pp. 83-100
Distributed Artificial Intelligence techniques have evolved toward multi-agent systems (MASs) where agents solve specific problems. Bargaining is a challenging area well-explored in both MAS and economics. To make agents more human-like and to increase their flexibility to reach an agreement, the authors investigated the role of personality behaviors of participants in a multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. The authors simulate a computational approach based on a heuristic bargaining protocol and personality model on artificial stereotypes. The results suggest compound behaviors appropriate to gain the best overall utility in the role of buyer and seller and with regard to social welfare and market activeness. This generic personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in areas involving negotiation and bargaining. © 2008 Sage Publications.
This paper presents a model for cultural intelligent agent decision making. The proposed model is based on Schwartz 10 value type. We follow a fuzzy approach for identification of agent's values and cultural dimension. Each cultural value causes a set of behavior that according to its importance is performed by agent. In each situation agent selects nearest situation in comparison with its criteria. These criteria are explored by agent according to cultural values. We use of fuzzy J and aglet for implementation. © 2008 IEEE.
Frontiers in Artificial Intelligence and Applications (09226389) 177(1)pp. 87-98
Negotiation is a process between self-interested agents trying to reach an agreement on one or multiple issues in an ecommerce domain. The knowledge of an agent about the opponents' strategies improves the negotiation outcome. However, an agent negotiates with incomplete information about its opponent. Given this, to detect the opponent's strategy, we can use the similarity between opponents' strategies. In this paper we present a method for measuring the similarity between negotiators' strategies. Offers are generated by the agent's strategy therefore our similarity measure is based on the history of offers in negotiation sessions. We extended the Levenshtein distance technique to detect similarity between strategies. We implement this measure and experimentally show that the result of using the measure improves the recognition of the opponent's strategy. © 2008 The authors and IOS Press. All rights reserved.
Lecture Notes in Electrical Engineering (18761119) 6pp. 297-307
Automated negotiation is a key form of interaction in complex systems composed of autonomous agents. Negotiation is a process of making offers and counteroffers, with the aim of finding an acceptable agreement [1]. The agents (negotiators) decide for themselves what actions they should perform, at what time, and under what terms and conditions [1, 2]. The outcome of the negotiation depends on several parameters such as the agents' strategies and the knowledge which one agent has about the opponents [2-5]. In recent years, the problem of modeling and predicting negotiator behavior has become increasingly important because this can be used to improve negotiation outcome and increase satisfaction of results [2-6]. In this chapter we consider the problem of defining strategies' similarity or distance between strategies. We start with the idea that similarity between negotiators should somehow reflect the amount of work that has to be done to convert one negotiation session to another. We formalize this notion as Levenshtein or edit distance [8, 9] between negotiations. We apply dynamic programming for computing the edit distances and show the resulting algorithm is efficient in practice. Indetail, the chapter is organized as follows. In Sect. 22.2 we present the problem in negotiations. The definition of similarity between negotiation strategies is given in Sect. 22.3. In Sect. 22.4 we review the negotiation protocol used in our experimentation. We use some negotiation strategies in our simulation discussed in Sect. 22.5. In Sect. 22.6 we present some results of computing similarity measures. Section 22.7 contains conclusions and remarks about future directions. © 2008 Springer Science+Business Media, LLC.
The major drawback of fuzzy data mining is that after applying fuzzy data mining on the quantitative data, the number of extracted fuzzy association rules is very huge. When many association rules are obtained, the usefulness of them will be reduced. In this paper, we introduce an approach to reduce and summarize the extracted fuzzy association rules after fuzzy data mining. In our approach, in first, we encode each obtained fuzzy association rule to a string of numbers. Then we use self-organizing map (SOM) neural network iteratively in a tree structure for clustering these encoded rules and summarizing them to a smaller collection of fuzzy association rules. This approach has been applied on a data base containing information about 5000 employees and has shown good results. © 2008 IADIS.
The bargaining problem in two-person games is selecting a particular point (i.e., an equilibrium) in the utility set to reach a jointly optimal result. Nonetheless, there are games with no or even more than one equilibrium, where in non-zero-sum games with multiple equilibria, all equilibria do not necessarily result in the same utility set. Each player not only should play with her/his equilibrium strategies, but also (s)he would be better to select the strategy that leads her/him to a better utility than the other equilibria. We present a BNE approach consisting of three parts: a 2-layer graph representation of the game that encompasses both strategic and extensive representations; a game reduction method where all Nash Equilibria remain unchanged; and locating the best NE strategy (which results in the best social welfare among all NEs) to start with, along with determining the first mover. The study shows the computationally correctness of the approach in well-known 2×2 and different sizes of sample 2-person games.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 4749pp. 404-409
In this paper we have proposed an approach to extend the existing service-oriented architecture reference model by taking into consideration the hierarchical human needs model, which can help us in determining the user's goals and enhancing the service discovery process. This is achieved by enriching the user's context model and representing the needs model as a specific ontology. The main benefits of this approach are improved service matching, and ensuring better privacy as required by users in utilizing specific services like profile-matching. © Springer-Verlag Berlin Heidelberg 2007.
Scientia Iranica (23453605) 14(6)pp. 631-640
In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/ buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents. © Sharif University of Technology, December 2007.
Journal of Theoretical and Applied Electronic Commerce Research (07181876) 2(1)pp. 1-17
In this paper, we propose a market model which Is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. © 2007 Universidad de Talca.
Malaysian Journal Of Computer Science (01279084) 20(1)pp. 35-50
When enterprises collaborate, a common frame of understanding of all products and catalogs in each organization is indispensable. Suppliers who virtually collaborate should be able to share product related data, create new products, and update old products from their own catalog. In this paper, we describe the development of a model for presenting electronic catalog based on OWL ontology language. This model uses WordNet ontology to distinguish classes and the relationships between them. We use SPARQL query language to introduce three types of search for the catalog management system. The concepts of this classification system are mapped to the concept of current standard classification systems such as UNSPSC, ECLΣS, and etc. We use VSM to diagnose class of one product. For customization aspect of electronic catalog, we introduce a semantic recommendation procedure which is more efficient when applied to Internet shopping malls. The suggested procedure recommends the semantic products to the customers and is originally based on Web usage mining, product classification, association rule mining, and frequently purchasing. We applied the procedure to the data set of MovieLens Company for performance evaluation, and some experimental results are provided. The experimental results have shown superior performance in terms of coverage and precision.
Lecture Notes in Engineering and Computer Science (20780958) pp. 859-863
Negotiation is a process between self-interested agents in ecommerce trying to reach an agreement on one or multi issues. The outcome of the negotiation depends on several parameters such as the agents' strategies and the knowledge one agent has about the opponents. One way for discovering opponent's strategy is to find the similarity between strategies. In this paper we present a simple model for measuring the similarity of negotiators' strategies. Our measure is based only on the history of the offers during the sessions of negotiation and we use a notion of Levenshtein distance. We implement this measure and experimentally show that the result of using this measure can improve the recognition of negotiation strategy. Also, this measure can be used for modeling behaviors of negotiators and predictive decision-making.
Lecture Notes in Engineering and Computer Science (20780958) pp. 845-849
The development of electronic marketing has contributed to present models and structures for marketing strategies. Personalization is an inseparable portion of electronic marketing and has contributed to models and structures of marketing .The presented personalization models do not have a comprehensive structure and can not cover marketing domains of all services and goods, and also have not a considerable precision for predicting customer's behavior. In this paper, a personalization model is presented based on the theoretical fundamentals of marketing and the known concept of 4P Marketing Mix. In comparison with other models, our mode) will cover all electronic marketing domains of goods and services, and also it will provide a more precision for predicting the customer's behavior.
