CAViAR is a mobile software system for indoor environments that provides to the mobile user equipped with a smartphone indoor localization, augmented reality (AR), visual interaction, and indoor navigation. These capabilities are possible with the availability of state of the art AR technologies. The mobile application includes additional features, such as indoor maps, shortest path, inertial navigation, places of interest, location sharing and voice-commanded search. CAViAR was tested in a University Campus as one of the technologies to be used later in an intelligent Campus environment.
{"title":"CAViAR: Context Aware Visual Indoor Augmented Reality for a University Campus","authors":"Buti Al Delail, L. Weruaga, M. Zemerly","doi":"10.1109/WI-IAT.2012.99","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.99","url":null,"abstract":"CAViAR is a mobile software system for indoor environments that provides to the mobile user equipped with a smartphone indoor localization, augmented reality (AR), visual interaction, and indoor navigation. These capabilities are possible with the availability of state of the art AR technologies. The mobile application includes additional features, such as indoor maps, shortest path, inertial navigation, places of interest, location sharing and voice-commanded search. CAViAR was tested in a University Campus as one of the technologies to be used later in an intelligent Campus environment.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130659358","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}
We propose a novel method to improve the prediction accuracy on the rating prediction task by correcting the bias of user ratings. We demonstrate that the manner of user rating and review is biased and that it is necessary to correct this difference for more accurate prediction. Our proposed method comprises approaches based on the detection of each user value to ratings: The bias of the rating is detected using entropy of user rating and by updating word weights only when the words appear in the review, the problem of bias is reduced. We implement this idea by extending the Prank algorithm. We apply a review -- item matrix as a feature matrix instead of a user -- item matrix because of its volume of information. Our quantitative evaluation shows that our method improves the prediction accuracy (the Rank Loss measurement) significantly by 8.70 % compared with the normal Prank algorithm. Our proposed method helps users find out what they care about when buying something, and is applicable to newer variants of the Prank algorithm. Moreover, it is useful to most review sites because we use only rating and review data.
{"title":"Rating Prediction by Correcting User Rating Bias","authors":"Masanao Ochi, Y. Matsuo, Makoto Okabe, R. Onai","doi":"10.1109/WI-IAT.2012.186","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.186","url":null,"abstract":"We propose a novel method to improve the prediction accuracy on the rating prediction task by correcting the bias of user ratings. We demonstrate that the manner of user rating and review is biased and that it is necessary to correct this difference for more accurate prediction. Our proposed method comprises approaches based on the detection of each user value to ratings: The bias of the rating is detected using entropy of user rating and by updating word weights only when the words appear in the review, the problem of bias is reduced. We implement this idea by extending the Prank algorithm. We apply a review -- item matrix as a feature matrix instead of a user -- item matrix because of its volume of information. Our quantitative evaluation shows that our method improves the prediction accuracy (the Rank Loss measurement) significantly by 8.70 % compared with the normal Prank algorithm. Our proposed method helps users find out what they care about when buying something, and is applicable to newer variants of the Prank algorithm. Moreover, it is useful to most review sites because we use only rating and review data.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222678","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}
Jie Jiang, Virginia Dignum, Shengyu Wu, Yao-Hua Tan
Agent-based simulation is an important methodology for analysis and evaluation of multi-organizational interactions, as it has the advantages of describing complex relations between entities in dynamic environments. However, an effective simulation design requires a systematic way of modeling organizational interactions to make sure that the integrated interaction processes reflect the essential properties of the real-life system. OperA+, an agent-based organization modeling framework, due to its capability for analysis and communication of organizational knowledge at multiple abstraction levels in multiple contexts, provides a basis for agent-based simulation design used for policy analysis. In this paper, we demonstrate how OperA+ can be used to assist a simulation design by a scenario study in regulated international trades. The aim is to evaluate a new policy authorizing private companies to regulate themselves on behalf of governmental authorities.
{"title":"Agent-Based Modeling and Simulation of Multi-organizational Interactions in International Trade","authors":"Jie Jiang, Virginia Dignum, Shengyu Wu, Yao-Hua Tan","doi":"10.1109/WI-IAT.2012.40","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.40","url":null,"abstract":"Agent-based simulation is an important methodology for analysis and evaluation of multi-organizational interactions, as it has the advantages of describing complex relations between entities in dynamic environments. However, an effective simulation design requires a systematic way of modeling organizational interactions to make sure that the integrated interaction processes reflect the essential properties of the real-life system. OperA+, an agent-based organization modeling framework, due to its capability for analysis and communication of organizational knowledge at multiple abstraction levels in multiple contexts, provides a basis for agent-based simulation design used for policy analysis. In this paper, we demonstrate how OperA+ can be used to assist a simulation design by a scenario study in regulated international trades. The aim is to evaluate a new policy authorizing private companies to regulate themselves on behalf of governmental authorities.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711862","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}
Computer programming is a highly practical subject and it is essential that those new to the discipline engage in hands-on experimentation as part of the learning process. However, when faced with large cohorts and an increasing demand for distance and student flexible learning, incorporating this into a programming course can be difficult. There is a dynamic that exists between tutor and student in a real-world programming workshop session that is not easily replicated online. In this paper we describe an online learning environment that begins to create an analogue of this dynamic and its successful integration into an undergraduate programming module. Ultimately, the potential exists to not only improve the student learning experience but also investigate and inform programming pedagogy itself.
{"title":"NoobLab: An Intelligent Learning Environment for Teaching Programming","authors":"P. Neve, G. Hunter, D. Livingstone, J. Orwell","doi":"10.1109/WI-IAT.2012.218","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.218","url":null,"abstract":"Computer programming is a highly practical subject and it is essential that those new to the discipline engage in hands-on experimentation as part of the learning process. However, when faced with large cohorts and an increasing demand for distance and student flexible learning, incorporating this into a programming course can be difficult. There is a dynamic that exists between tutor and student in a real-world programming workshop session that is not easily replicated online. In this paper we describe an online learning environment that begins to create an analogue of this dynamic and its successful integration into an undergraduate programming module. Ultimately, the potential exists to not only improve the student learning experience but also investigate and inform programming pedagogy itself.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121345275","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}
Crowd sourcing (CS) systems offer a new way for businesses and individuals to leverage on the power of mass collaboration to accomplish complex tasks in a divide-and-conquer manner. In existing CS systems, no facility has been provided for analyzing the trustworthiness of workers and providing decision support for allocating tasks to workers, which leads to high dependency of the quality of work on the behavior of workers in CS systems as shown in this paper. To address this problem, trust management mechanisms are urgently needed. Traditional trust management techniques are focused on identifying the most trustworthy service providers (SPs) as accurately as possible. Little thoughts were given to the question of how to utilize these SPs due to two common assumptions: 1) an SP can serve an unlimited number of requests in one time unit, and 2) a service consumer (SC) only needs to select one SP for interaction to complete a task. However, in CS systems, these two assumptions are no longer valid. Thus, existing models cannot be directly used for trust management in CS systems. This paper takes the first step towards a systematic investigation of trust management in CS systems by extending existing trust management models for CS trust management and conducting extensive experiments to study and analyze the performance of various trust management models in crowd sourcing. In this paper, the following key contributions are made. We 1) propose extensions to existing trust management approaches to enable them to operate in CS systems, 2) design a simulation test-bed based on the system characteristics of Amazon's Mechanical Turk (AMT) to make evaluation close to practical CS systems, 3) discuss the effect of incorporating trust management into CS system on the overall social welfare, and 4) identify the challenges and opportunities for future trust management research in CS systems.
{"title":"Challenges and Opportunities for Trust Management in Crowdsourcing","authors":"Han Yu, Zhiqi Shen, C. Miao, Bo An","doi":"10.1109/WI-IAT.2012.104","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.104","url":null,"abstract":"Crowd sourcing (CS) systems offer a new way for businesses and individuals to leverage on the power of mass collaboration to accomplish complex tasks in a divide-and-conquer manner. In existing CS systems, no facility has been provided for analyzing the trustworthiness of workers and providing decision support for allocating tasks to workers, which leads to high dependency of the quality of work on the behavior of workers in CS systems as shown in this paper. To address this problem, trust management mechanisms are urgently needed. Traditional trust management techniques are focused on identifying the most trustworthy service providers (SPs) as accurately as possible. Little thoughts were given to the question of how to utilize these SPs due to two common assumptions: 1) an SP can serve an unlimited number of requests in one time unit, and 2) a service consumer (SC) only needs to select one SP for interaction to complete a task. However, in CS systems, these two assumptions are no longer valid. Thus, existing models cannot be directly used for trust management in CS systems. This paper takes the first step towards a systematic investigation of trust management in CS systems by extending existing trust management models for CS trust management and conducting extensive experiments to study and analyze the performance of various trust management models in crowd sourcing. In this paper, the following key contributions are made. We 1) propose extensions to existing trust management approaches to enable them to operate in CS systems, 2) design a simulation test-bed based on the system characteristics of Amazon's Mechanical Turk (AMT) to make evaluation close to practical CS systems, 3) discuss the effect of incorporating trust management into CS system on the overall social welfare, and 4) identify the challenges and opportunities for future trust management research in CS systems.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122320974","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}
With the social networks (SNs) becoming ubiquitous and massive, the issue of similarity computation among entities becomes more challenging and draws extensive interests from various research fields. SimRank is a well known similarity measure, however it considers only the meetings between two nodes that walk along equal length paths since the path length increases strictly with the iteration increasing during the similarity computation, besides, it does not differentiate importance for each link. In this paper, we propose a novel structural similarity measure, E-Rank (Entity Rank), towards effectively computing the structural similarity of entities in SNs, based on the intuition that two entities are similar if they can arrive at common entities. E-Rank can be well applied to social networks for measuring similarities of entities. Extensive experiments demonstrate the effectiveness of E-Rank by comparing with the state-of-the-art measures.
{"title":"E-rank: A Structural-Based Similarity Measure in Social Networks","authors":"Mingxi Zhang, Zhenying He, Hao Hu, Wei Wang","doi":"10.1109/WI-IAT.2012.111","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.111","url":null,"abstract":"With the social networks (SNs) becoming ubiquitous and massive, the issue of similarity computation among entities becomes more challenging and draws extensive interests from various research fields. SimRank is a well known similarity measure, however it considers only the meetings between two nodes that walk along equal length paths since the path length increases strictly with the iteration increasing during the similarity computation, besides, it does not differentiate importance for each link. In this paper, we propose a novel structural similarity measure, E-Rank (Entity Rank), towards effectively computing the structural similarity of entities in SNs, based on the intuition that two entities are similar if they can arrive at common entities. E-Rank can be well applied to social networks for measuring similarities of entities. Extensive experiments demonstrate the effectiveness of E-Rank by comparing with the state-of-the-art measures.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126071652","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}
Chunjing Xiao, Ling Su, J. Bi, Yuxia Xue, A. Kuzmanovic
According to the classical communication theories, known as Gate keeping and Selective Exposure, individuals tend to have selective behavior when they disseminate and receive information based on their psychological preferences. Selective behavior related to these two theories have been broadly studied separately. While, thanks to the advent of Online Social Networks (OSNs), larger-scale feedback and user information can be collected. In this paper, based on these data, We analyze the correlation among users' properties (such as age, gender, and cultural background) and analyze their selective behavior by tagging users as disseminators and/or audiences in YouTube, Flickr, and Twitter. We find that despite enormous amount of content available in OSNs, users have a comparatively small selective range and do exhibit selective behavior properties. In particular, they pay the most attention to the content published by disseminators that share similar properties, i.e., gender, age, and country. Nonetheless, we also find significant differences and commonalities among the three OSNs with respect to selective behavior. In particular, (i) the proportion and properties of disseminators, audiences, and dual-role users are quite different for the three networks, (ii) the global level of information spread in Flickr is almost two times than that in Twitter and YouTube is approximately the median one, (iii) For a given country, the global level of information spread is different for different OSNs. For a given OSN, it is different for different countries, (iv) despite ubiquitous presence of dual-role users in OSNs, most of such users are very active as either disseminators or audiences, but not both. Our findings are not only useful for understanding these two theories, but also have applications ranging from advertising and recommendation systems to developing predicting models.
{"title":"Selective Behavior in Online Social Networks","authors":"Chunjing Xiao, Ling Su, J. Bi, Yuxia Xue, A. Kuzmanovic","doi":"10.1109/WI-IAT.2012.42","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.42","url":null,"abstract":"According to the classical communication theories, known as Gate keeping and Selective Exposure, individuals tend to have selective behavior when they disseminate and receive information based on their psychological preferences. Selective behavior related to these two theories have been broadly studied separately. While, thanks to the advent of Online Social Networks (OSNs), larger-scale feedback and user information can be collected. In this paper, based on these data, We analyze the correlation among users' properties (such as age, gender, and cultural background) and analyze their selective behavior by tagging users as disseminators and/or audiences in YouTube, Flickr, and Twitter. We find that despite enormous amount of content available in OSNs, users have a comparatively small selective range and do exhibit selective behavior properties. In particular, they pay the most attention to the content published by disseminators that share similar properties, i.e., gender, age, and country. Nonetheless, we also find significant differences and commonalities among the three OSNs with respect to selective behavior. In particular, (i) the proportion and properties of disseminators, audiences, and dual-role users are quite different for the three networks, (ii) the global level of information spread in Flickr is almost two times than that in Twitter and YouTube is approximately the median one, (iii) For a given country, the global level of information spread is different for different OSNs. For a given OSN, it is different for different countries, (iv) despite ubiquitous presence of dual-role users in OSNs, most of such users are very active as either disseminators or audiences, but not both. Our findings are not only useful for understanding these two theories, but also have applications ranging from advertising and recommendation systems to developing predicting models.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121208779","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}
In this paper, we discuss about a preliminary idea and an analysis about a dynamic electric power auction for manufacturing industries. We present a preliminary idea about applying multi-unit combinatorial auctions to an electric power allocation problem that considers guaranteeing stable continuous use of the supplied power in industrial manufacturers. We try to illustrate how such a mechanism can be applied to actual electric power allocation problems when we consider the situation that each manufacturing factories produce bids based on their own plans of production and the use of electricity in a day, guaranteeing stable continuous use of them. An approximation mechanism has been applied for a large-scale auction problem to overcome its computational intractability. We discuss about a possible performance based on our proposed evaluation dataset which consider actual power use scenarios in industrial factories.
{"title":"A Preliminary Experimental Analysis on Combinatorial Auction-Based Electric Power Allocation for Manufacturing Industries","authors":"Naoki Fukuta, Takayuki Ito","doi":"10.1109/WI-IAT.2012.221","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.221","url":null,"abstract":"In this paper, we discuss about a preliminary idea and an analysis about a dynamic electric power auction for manufacturing industries. We present a preliminary idea about applying multi-unit combinatorial auctions to an electric power allocation problem that considers guaranteeing stable continuous use of the supplied power in industrial manufacturers. We try to illustrate how such a mechanism can be applied to actual electric power allocation problems when we consider the situation that each manufacturing factories produce bids based on their own plans of production and the use of electricity in a day, guaranteeing stable continuous use of them. An approximation mechanism has been applied for a large-scale auction problem to overcome its computational intractability. We discuss about a possible performance based on our proposed evaluation dataset which consider actual power use scenarios in industrial factories.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116555155","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}
In order to directly reason over inconsistent OWL 2 DL ontologies, this paper considers linear order inference which comes from propositional logic. Consequences of this inference in an inconsistent ontology are defined as consequences in a certain consistent sub-ontology. This paper proposes a novel framework for compiling an OWL 2 DL ontology to a Horn propositional program so that the intended consistent sub-ontology for linear order inference can be approximated from the compiled result in polynomial time. A tractable method is proposed to realize this framework. It guarantees that the compiled result has a polynomial size. Experimental results show that the proposed method computes the exact intended sub-ontology for almost all test cases, while it is significantly more efficient and scalable than state-of-the-art exact methods.
{"title":"Approximating Linear Order Inference in OWL 2 DL by Horn Compilation","authors":"Jianfeng Du, G. Qi, Jeff Z. Pan, Yi-Dong Shen","doi":"10.1109/WI-IAT.2012.11","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.11","url":null,"abstract":"In order to directly reason over inconsistent OWL 2 DL ontologies, this paper considers linear order inference which comes from propositional logic. Consequences of this inference in an inconsistent ontology are defined as consequences in a certain consistent sub-ontology. This paper proposes a novel framework for compiling an OWL 2 DL ontology to a Horn propositional program so that the intended consistent sub-ontology for linear order inference can be approximated from the compiled result in polynomial time. A tractable method is proposed to realize this framework. It guarantees that the compiled result has a polynomial size. Experimental results show that the proposed method computes the exact intended sub-ontology for almost all test cases, while it is significantly more efficient and scalable than state-of-the-art exact methods.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131634780","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}
Building domain taxonomies is a crucial task in the domain of ontology construction. Domain taxonomy learning keeps getting more important as a form of automatically obtaining a knowledge representation of a certain domain. The alternative of manually developing domain taxonomies is not trivial. The main issues encountered when manually developing a taxonomy are the non-availability of a domain knowledge expert and the considerable amount of effort needed for this task. This paper proposes Taxo Learn, an approach to automatic construction of domain taxonomies. Taxo Learn is a new methodology that combines aspects from existing approaches, but also contains new steps in order to improve the quality of the resulted domain taxonomy. The contribution of this paper is threefold. First, we employ a word sense disambiguation step when detecting concepts in the text. Second, we show the use of semantics-based hierarchical clustering for the purpose of taxonomy learning. Third, we propose a novel dynamic labeling procedure for the concept clusters. We evaluate our approach by comparing the machine generated taxonomy with a manually constructed golden taxonomy. Based on a corpus of documents in the field of financial economics, Taxo Learn shows a high precision for the learned taxonomic concept relationships.
{"title":"TaxoLearn: A Semantic Approach to Domain Taxonomy Learning","authors":"Emmanuelle-Anna Dietz, Damir Vandic, F. Frasincar","doi":"10.1109/WI-IAT.2012.129","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.129","url":null,"abstract":"Building domain taxonomies is a crucial task in the domain of ontology construction. Domain taxonomy learning keeps getting more important as a form of automatically obtaining a knowledge representation of a certain domain. The alternative of manually developing domain taxonomies is not trivial. The main issues encountered when manually developing a taxonomy are the non-availability of a domain knowledge expert and the considerable amount of effort needed for this task. This paper proposes Taxo Learn, an approach to automatic construction of domain taxonomies. Taxo Learn is a new methodology that combines aspects from existing approaches, but also contains new steps in order to improve the quality of the resulted domain taxonomy. The contribution of this paper is threefold. First, we employ a word sense disambiguation step when detecting concepts in the text. Second, we show the use of semantics-based hierarchical clustering for the purpose of taxonomy learning. Third, we propose a novel dynamic labeling procedure for the concept clusters. We evaluate our approach by comparing the machine generated taxonomy with a manually constructed golden taxonomy. Based on a corpus of documents in the field of financial economics, Taxo Learn shows a high precision for the learned taxonomic concept relationships.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116404","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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