This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.
{"title":"Predicting Researchers' Future Activities Using Visualization System for Co-authorship Networks","authors":"Takeshi Kurosawa, Y. Takama","doi":"10.1109/WI-IAT.2011.96","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.96","url":null,"abstract":"This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper focuses on the problem of discovering users' topics of interest on Twitter. While previous efforts in modeling users' topics of interest on Twitter have focused on building a "bag-of-words" profile for each user based on his tweets, they overlooked the fact that Twitter users usually publish noisy posts about their lives or create conversation with their friends, which do not relate to their topics of interest. In this paper, we propose a novel framework to address this problem by introducing a modified author-topic model named twitter-user model. For each single tweet, our model uses a latent variable to indicate whether it is related to its author's interest. Experiments on a large dataset we crawled using Twitter API demonstrate that our model outperforms traditional methods in discovering user interest on Twitter.
{"title":"Discovering User Interest on Twitter with a Modified Author-Topic Model","authors":"Zhiheng Xu, Rong Lu, Liang Xiang, Qing Yang","doi":"10.1109/WI-IAT.2011.47","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.47","url":null,"abstract":"This paper focuses on the problem of discovering users' topics of interest on Twitter. While previous efforts in modeling users' topics of interest on Twitter have focused on building a \"bag-of-words\" profile for each user based on his tweets, they overlooked the fact that Twitter users usually publish noisy posts about their lives or create conversation with their friends, which do not relate to their topics of interest. In this paper, we propose a novel framework to address this problem by introducing a modified author-topic model named twitter-user model. For each single tweet, our model uses a latent variable to indicate whether it is related to its author's interest. Experiments on a large dataset we crawled using Twitter API demonstrate that our model outperforms traditional methods in discovering user interest on Twitter.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the current state of the legal systems, Online Dispute Resolution tools are being regarded as an alternative way to solve conflicts out of courts, namely under virtual environments. However, the use of these tools is still relatively restricted as they are still few in number and very domain-cantered. Indeed, abstract and conceptual tools whose building blocks could be adapted for particular use would foster the development of ODR systems. In this paper we present this novel line of attack, in which an agent-based architecture is used with the support of an ontology to build an abstract and formal ODR system, independent of the legal domains, but specific enough to be applied to concrete ones. Functionality reuse is maximized, making architectures simpler to implement and to expand.
{"title":"Towards Domain-Independent Conflict Resolution Tools","authors":"Davide Carneiro, P. Novais, J. Neves","doi":"10.1109/WI-IAT.2011.84","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.84","url":null,"abstract":"Given the current state of the legal systems, Online Dispute Resolution tools are being regarded as an alternative way to solve conflicts out of courts, namely under virtual environments. However, the use of these tools is still relatively restricted as they are still few in number and very domain-cantered. Indeed, abstract and conceptual tools whose building blocks could be adapted for particular use would foster the development of ODR systems. In this paper we present this novel line of attack, in which an agent-based architecture is used with the support of an ontology to build an abstract and formal ODR system, independent of the legal domains, but specific enough to be applied to concrete ones. Functionality reuse is maximized, making architectures simpler to implement and to expand.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121399510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semantic Web and ontology have received increased attention in recent years. Various tools on modularization have been developed. However, selecting an appropriate ontology to fit a given context is difficult because assumptions for the approaches vary greatly. Therefore, a method that can compare modularization tools and help screen the most suitable approach is highly desired. In this research, a new set of evaluation criteria for selecting appropriate ontology modularization tools is proposed. Three aspects of tool evaluation, namely, tool performance, data performance, and usability are presented. This study is an empirical analysis of a number of modularization tools. Experimental results indicate that the proposed evaluation criteria for ontology modularization tools are valid and effective. It provides an evaluation method for ontology modularization, enabling ontology engineers to compare different modularization tools and easily choose the appropriate approach to produce quality ontology modules.
{"title":"Evaluation Criteria Ontology Modularization Tools","authors":"Sunju Oh, H. Yeom","doi":"10.1109/WI-IAT.2011.107","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.107","url":null,"abstract":"Semantic Web and ontology have received increased attention in recent years. Various tools on modularization have been developed. However, selecting an appropriate ontology to fit a given context is difficult because assumptions for the approaches vary greatly. Therefore, a method that can compare modularization tools and help screen the most suitable approach is highly desired. In this research, a new set of evaluation criteria for selecting appropriate ontology modularization tools is proposed. Three aspects of tool evaluation, namely, tool performance, data performance, and usability are presented. This study is an empirical analysis of a number of modularization tools. Experimental results indicate that the proposed evaluation criteria for ontology modularization tools are valid and effective. It provides an evaluation method for ontology modularization, enabling ontology engineers to compare different modularization tools and easily choose the appropriate approach to produce quality ontology modules.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Harbers, J. Bradshaw, Matt Johnson, P. Feltovich, K. Bosch, J. Meyer
There are several applications in which humans and agents jointly perform a task. If the task involves interdependence among the team members, coordination is required to achieve good team performance. Coordination in human-agent teams can be improved by giving humans insight in the behavior of the agents. When humans are able to understand and predict an agent's behavior, they can more easily adapt their own behavior to that of the agent. One way to achieve such understanding is by letting agents explain their behavior. This paper presents a study in the BW4T coordination test bed that examines the effects of agents explaining their behavior on coordination in human-agent teams. The results show that explanations about agent behavior do not always lead to better team performance, but they do impact user experience in a positive way.
{"title":"Explanation and Coordination in Human-Agent Teams: A Study in the BW4T Testbed","authors":"M. Harbers, J. Bradshaw, Matt Johnson, P. Feltovich, K. Bosch, J. Meyer","doi":"10.1109/WI-IAT.2011.83","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.83","url":null,"abstract":"There are several applications in which humans and agents jointly perform a task. If the task involves interdependence among the team members, coordination is required to achieve good team performance. Coordination in human-agent teams can be improved by giving humans insight in the behavior of the agents. When humans are able to understand and predict an agent's behavior, they can more easily adapt their own behavior to that of the agent. One way to achieve such understanding is by letting agents explain their behavior. This paper presents a study in the BW4T coordination test bed that examines the effects of agents explaining their behavior on coordination in human-agent teams. The results show that explanations about agent behavior do not always lead to better team performance, but they do impact user experience in a positive way.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125959459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data sharing services on the web host huge amounts of resources supplied and accessed by millions of users around the world. While the classical approach is a central control over the data set, even if this data set is distributed, there is growing interesting in decentralized solutions, because of good properties (in particularity, privacy and scaling up). In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr.
{"title":"A Decentralized and Robust Approach to Estimating a Probabilistic Mixture Model for Structuring Distributed Data","authors":"Ali El Attar, A. Pigeau, Marc Gelgon","doi":"10.1109/WI-IAT.2011.46","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.46","url":null,"abstract":"Data sharing services on the web host huge amounts of resources supplied and accessed by millions of users around the world. While the classical approach is a central control over the data set, even if this data set is distributed, there is growing interesting in decentralized solutions, because of good properties (in particularity, privacy and scaling up). In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing computational models of emotions, although based on different psychological theories, share common properties and may be seen as the different facets of a common emotional process. We thus present our model GRACE - aiming at unifying existing models into a single architecture while preserving the peculiarities of each of them. We also demonstrate the generality of GRACE in emulating the behavior of these existing models.
{"title":"Emotion Modeling for Intelligent Agents - Towards a Unifying Framework","authors":"T. Dang, G. Hutzler, P. Hoppenot","doi":"10.1109/WI-IAT.2011.155","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.155","url":null,"abstract":"Existing computational models of emotions, although based on different psychological theories, share common properties and may be seen as the different facets of a common emotional process. We thus present our model GRACE - aiming at unifying existing models into a single architecture while preserving the peculiarities of each of them. We also demonstrate the generality of GRACE in emulating the behavior of these existing models.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services' descriptions with users' features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? To this end, we exploit Description Logics reasoning through semantic matching to (i) identify useful parts of a user profile that satisfy services requirements (i.e., input parameters) and (ii) compute the description required by a service to be executed but not provided by the user profile. Our approach, part of the EC-funded project SOA4All, was evaluated on its applicability in real world scenarios with end-users.
{"title":"Towards Semantics-Based Instantiation of Services","authors":"F. Lécué","doi":"10.1109/WI-IAT.2011.211","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.211","url":null,"abstract":"Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services' descriptions with users' features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? To this end, we exploit Description Logics reasoning through semantic matching to (i) identify useful parts of a user profile that satisfy services requirements (i.e., input parameters) and (ii) compute the description required by a service to be executed but not provided by the user profile. Our approach, part of the EC-funded project SOA4All, was evaluated on its applicability in real world scenarios with end-users.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124373240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More and more personalization systems are emerging to reduce the information overload of the Web. As a result, it has become vital to model users' preferences accurately. Our focus lies in the quality of users' expressed preferences, in terms of reliability and stability through time. Today, users are often brought to express their preferences through ratings on a multi-point scale. However, several studies have highlighted problems with ratings. We propose a new preference modality whereby users compare items two-by two ("I prefer x to y").This initial work on comparisons shows that users are in favor of this new preference mechanism and that comparisons are almost 20% more stable over time than those conveyed through ratings, thus more reliable. These encouraging findings let us think that comparisons may lead to a better user modeling and an increase in the quality of personalization services, such as recommender systems.
{"title":"Comparisons Instead of Ratings: Towards More Stable Preferences","authors":"Nicolas Jones, A. Brun, A. Boyer","doi":"10.1109/WI-IAT.2011.13","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.13","url":null,"abstract":"More and more personalization systems are emerging to reduce the information overload of the Web. As a result, it has become vital to model users' preferences accurately. Our focus lies in the quality of users' expressed preferences, in terms of reliability and stability through time. Today, users are often brought to express their preferences through ratings on a multi-point scale. However, several studies have highlighted problems with ratings. We propose a new preference modality whereby users compare items two-by two (\"I prefer x to y\").This initial work on comparisons shows that users are in favor of this new preference mechanism and that comparisons are almost 20% more stable over time than those conveyed through ratings, thus more reliable. These encouraging findings let us think that comparisons may lead to a better user modeling and an increase in the quality of personalization services, such as recommender systems.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123523952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tasks of feature-level opinion mining usually include the extraction of product entities from product reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions' polarities (e.g., positive, negative, or neutral). In recent years, several approaches have been proposed such as rule-based and statistical methods on this subject, but few attentions have been paid to applying more discriminative learning models to achieve the goal. On the other hand, little work has evaluated their algorithms' performance for identifying intensifiers, entity phrases and infrequent entities. In this paper, we in particular adopt the Conditional Random Fields (CRFs) model to perform the opinion mining tasks. Relative to related approaches, we have not only highlighted the algorithm's ability in mining intensifiers, phrases and infrequent entities, but also integrated more elements in the model so as to optimize its training and decoding process. Our method was compared to the lexicalized Hidden Markov Model (L-HMMs) based opinion mining method in the experiment, which proves its significantly better accuracy from several aspects.
{"title":"Comparison of Model-Based Learning Methods for Feature-Level Opinion Mining","authors":"Luole Qi, Li Chen","doi":"10.1109/WI-IAT.2011.64","DOIUrl":"https://doi.org/10.1109/WI-IAT.2011.64","url":null,"abstract":"The tasks of feature-level opinion mining usually include the extraction of product entities from product reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions' polarities (e.g., positive, negative, or neutral). In recent years, several approaches have been proposed such as rule-based and statistical methods on this subject, but few attentions have been paid to applying more discriminative learning models to achieve the goal. On the other hand, little work has evaluated their algorithms' performance for identifying intensifiers, entity phrases and infrequent entities. In this paper, we in particular adopt the Conditional Random Fields (CRFs) model to perform the opinion mining tasks. Relative to related approaches, we have not only highlighted the algorithm's ability in mining intensifiers, phrases and infrequent entities, but also integrated more elements in the model so as to optimize its training and decoding process. Our method was compared to the lexicalized Hidden Markov Model (L-HMMs) based opinion mining method in the experiment, which proves its significantly better accuracy from several aspects.","PeriodicalId":128421,"journal":{"name":"2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123646209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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