Xiaoyuan Su, R. Greiner, T. Khoshgoftaar, Xingquan Zhu
Collaborative filtering (CF) is one of the most successful approaches for recommendation. In this paper, we propose two hybrid CF algorithms, sequential mixture CF and joint mixture CF, each combining advice from multiple experts for effective recommendation. These proposed hybrid CF models work particularly well in the common situation when data are very sparse. By combining multiple experts to form a mixture CF, our systems are able to cope with sparse data to obtain satisfactory performance. Empirical studies show that our algorithms outperform their peers, such as memory-based, pure model-based, pure content-based CF algorithms, and the content- boosted CF (a representative hybrid CF algorithm), especially when the underlying data are very sparse.
{"title":"Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts","authors":"Xiaoyuan Su, R. Greiner, T. Khoshgoftaar, Xingquan Zhu","doi":"10.1109/WI.2007.136","DOIUrl":"https://doi.org/10.1109/WI.2007.136","url":null,"abstract":"Collaborative filtering (CF) is one of the most successful approaches for recommendation. In this paper, we propose two hybrid CF algorithms, sequential mixture CF and joint mixture CF, each combining advice from multiple experts for effective recommendation. These proposed hybrid CF models work particularly well in the common situation when data are very sparse. By combining multiple experts to form a mixture CF, our systems are able to cope with sparse data to obtain satisfactory performance. Empirical studies show that our algorithms outperform their peers, such as memory-based, pure model-based, pure content-based CF algorithms, and the content- boosted CF (a representative hybrid CF algorithm), especially when the underlying data are very sparse.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887394","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}
N. Neubauer, Christian Scheel, S. Albayrak, K. Obermayer
Feedback on past queries is a valuable resource for improving retrieval performance on new queries. We introduce a modular approach to incorporating feedback information into given retrieval architectures. We propose to fusion the original ranking with those returned by rerankers, each of which trained on feedback given for a distinct, single query. Here, we examine the basic case of improving a query's original ranking qtest by only using one reranker: the one trained on feedback on the "closest" query qtrain. We examine the use of various distance measures between queries to first identify qtrain and then determine the best linear combination of the original and the reranker's ratings, that is: to find out which feedback to learn from, and how strongly to use it. We show the cosine distance between the term vectors of the two queries, each enriched by representations of the top N originally returned documents, to reliably answer both questions. The fusion performs equally well or better than a) always using only the original ranker or the reranker, b) selecting a hard distance threshold to decide between the two, or c) fusioning results with a ratio that is globally optimized, but fixed across all tested queries.
{"title":"Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries","authors":"N. Neubauer, Christian Scheel, S. Albayrak, K. Obermayer","doi":"10.1109/WI.2007.48","DOIUrl":"https://doi.org/10.1109/WI.2007.48","url":null,"abstract":"Feedback on past queries is a valuable resource for improving retrieval performance on new queries. We introduce a modular approach to incorporating feedback information into given retrieval architectures. We propose to fusion the original ranking with those returned by rerankers, each of which trained on feedback given for a distinct, single query. Here, we examine the basic case of improving a query's original ranking qtest by only using one reranker: the one trained on feedback on the \"closest\" query qtrain. We examine the use of various distance measures between queries to first identify qtrain and then determine the best linear combination of the original and the reranker's ratings, that is: to find out which feedback to learn from, and how strongly to use it. We show the cosine distance between the term vectors of the two queries, each enriched by representations of the top N originally returned documents, to reliably answer both questions. The fusion performs equally well or better than a) always using only the original ranker or the reranker, b) selecting a hard distance threshold to decide between the two, or c) fusioning results with a ratio that is globally optimized, but fixed across all tested queries.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654438","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}
Knowledge incorporation is one challenge in e- Commerce automated negotiation. In this paper, we describe a model of B2B negotiation using knowledge. We classify the types of knowledge namely general knowledge and negotiation knowledge, in the negotiation process. A methodology that uses knowledge bead (KB) and meta-KB as knowledge representation that would be suitable for the design of automated negotiation systems is discussed. An experimental prototype demonstrates that by incorporating knowledge into automated negotiation yields improved results.
{"title":"Semantic Tagging for Large-scale Content Management","authors":"Liming Chen, C. Roberts","doi":"10.1109/WI.2007.94","DOIUrl":"https://doi.org/10.1109/WI.2007.94","url":null,"abstract":"Knowledge incorporation is one challenge in e- Commerce automated negotiation. In this paper, we describe a model of B2B negotiation using knowledge. We classify the types of knowledge namely general knowledge and negotiation knowledge, in the negotiation process. A methodology that uses knowledge bead (KB) and meta-KB as knowledge representation that would be suitable for the design of automated negotiation systems is discussed. An experimental prototype demonstrates that by incorporating knowledge into automated negotiation yields improved results.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116907990","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}
Web service selection is an essential element in service-oriented computing. How to wisely select appropriate Web services for the benefits of service consumers is a key issue in service discovery. In this paper, we approach QoS-based service selection using a decision making model - the analytic hierarchy process (AHP). In our solution, both subjective and objective criteria are supported by the AHP engine in a context-specific manner. We also provide a flexible Wiki platform to collaboratively form the initial QoS model within a service community. The software prototype is evaluated against the system scalability.
{"title":"Intelligent Web Services Selection based on AHP and Wiki","authors":"Chen Wu, E. Chang","doi":"10.1109/WI.2007.138","DOIUrl":"https://doi.org/10.1109/WI.2007.138","url":null,"abstract":"Web service selection is an essential element in service-oriented computing. How to wisely select appropriate Web services for the benefits of service consumers is a key issue in service discovery. In this paper, we approach QoS-based service selection using a decision making model - the analytic hierarchy process (AHP). In our solution, both subjective and objective criteria are supported by the AHP engine in a context-specific manner. We also provide a flexible Wiki platform to collaboratively form the initial QoS model within a service community. The software prototype is evaluated against the system scalability.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132306831","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}
An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.
{"title":"Automatic Taxonomy Extraction Using Google and Term Dependency","authors":"M. Makrehchi, M. Kamel","doi":"10.1109/WI.2007.26","DOIUrl":"https://doi.org/10.1109/WI.2007.26","url":null,"abstract":"An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124107362","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}
Recent work in the field of middleware technology proposes semantic spaces as a tool for coping with the scalability, heterogeneity and dynamism issues arising in large scale distributed environments. Reflective middleware moreover offers answers to the needs for adaptivity and selfdetermination of systems where mobility and ubiquity add to such environments. Based on experiences with traditional middleware we argue that ontology-driven management is a major advancement for semantic spaces and provides the fundamental means for reflection. By means of ontologies, and ontology-based reasoning services we can implement automatic adaptation of the middleware's functionality to environmental changes and user desires.
{"title":"An Ontology-Driven Approach To Reflective Middleware","authors":"Reto Krummenacher, E. Simperl, D. Fensel","doi":"10.1109/WI.2007.20","DOIUrl":"https://doi.org/10.1109/WI.2007.20","url":null,"abstract":"Recent work in the field of middleware technology proposes semantic spaces as a tool for coping with the scalability, heterogeneity and dynamism issues arising in large scale distributed environments. Reflective middleware moreover offers answers to the needs for adaptivity and selfdetermination of systems where mobility and ubiquity add to such environments. Based on experiences with traditional middleware we argue that ontology-driven management is a major advancement for semantic spaces and provides the fundamental means for reflection. By means of ontologies, and ontology-based reasoning services we can implement automatic adaptation of the middleware's functionality to environmental changes and user desires.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121202608","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, youreye, the real-time phrase recommender is introduced that suggests the related frequent phrases to the incomplete user query. The frequent phrases are extracted from within previous queries based on a new frequency rate metric suitable for query stream mining. The advantages of YourEye compared to Google suggest, a service powered by Google for phrase suggestion, is described. The experimental results also confirm the significant benefit of monitoring phrases instead of queries. The number of the monitored elements significantly reduces that results in smaller memory consumption as well as better performance.
{"title":"On Query Completion in Web Search Engines Based on Query Stream Mining","authors":"Jae-wook Ahn, Peter Brusilovsky","doi":"10.1109/WI.2007.80","DOIUrl":"https://doi.org/10.1109/WI.2007.80","url":null,"abstract":"In this paper, youreye, the real-time phrase recommender is introduced that suggests the related frequent phrases to the incomplete user query. The frequent phrases are extracted from within previous queries based on a new frequency rate metric suitable for query stream mining. The advantages of YourEye compared to Google suggest, a service powered by Google for phrase suggestion, is described. The experimental results also confirm the significant benefit of monitoring phrases instead of queries. The number of the monitored elements significantly reduces that results in smaller memory consumption as well as better performance.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128693731","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}
Anne-Laure Ligozat, Brigitte Grau, Anne Vilnat, Isabelle Robba, Arnaud Grappy
Question answering (QA) aims at retrieving precise information from a large collection of documents, typically the Web. Different techniques can be used to find relevant information, and to compare these techniques, it is important to evaluate question answering systems. The objective of an Answer Validation task is to estimate the correctness of an answer returned by a QA system for a question, according to the text snippet given to support it. In this article, we present a lexical strategy for deciding if the snippets justify the answers, based on our own question answering system. We discuss our results, and show the possible extensions of our strategy.
{"title":"Lexical validation of answers in Question Answering","authors":"Anne-Laure Ligozat, Brigitte Grau, Anne Vilnat, Isabelle Robba, Arnaud Grappy","doi":"10.1109/WI.2007.70","DOIUrl":"https://doi.org/10.1109/WI.2007.70","url":null,"abstract":"Question answering (QA) aims at retrieving precise information from a large collection of documents, typically the Web. Different techniques can be used to find relevant information, and to compare these techniques, it is important to evaluate question answering systems. The objective of an Answer Validation task is to estimate the correctness of an answer returned by a QA system for a question, according to the text snippet given to support it. In this article, we present a lexical strategy for deciding if the snippets justify the answers, based on our own question answering system. We discuss our results, and show the possible extensions of our strategy.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410773","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 present an advanced e-custom system pilot designed to address strategic goals for future custom systems. Future custom systems will support simplified paperless trade procedures, prevent potential security threats and counterfeit tax related fraud while at the same time insure interoperability with other e-custom systems within and outside Europe. The focus is placed on the advantages of use of novel technologies for the implementation of advanced e-custom systems. In particular we highlight the use of service oriented architecture (SOA), web services and TREC (Tamper Resistant Embedded Controller) device in an integrated framework named EPCIS (Electronic Product Code for Information Systems). Among the advantages of the presented solution are: the ubiquitous access to the location of goods through its supply chain, the provision of evidence for import/export, the notification through alerts in case of exceptions (such as deviation from the planned trajectory, abnormal conditions for containers, etc.).
{"title":"Towards Ubiquitous e-Custom Services","authors":"Liana Razmerita, N. Bjørn-Andersen","doi":"10.1109/WI.2007.151","DOIUrl":"https://doi.org/10.1109/WI.2007.151","url":null,"abstract":"In this paper we present an advanced e-custom system pilot designed to address strategic goals for future custom systems. Future custom systems will support simplified paperless trade procedures, prevent potential security threats and counterfeit tax related fraud while at the same time insure interoperability with other e-custom systems within and outside Europe. The focus is placed on the advantages of use of novel technologies for the implementation of advanced e-custom systems. In particular we highlight the use of service oriented architecture (SOA), web services and TREC (Tamper Resistant Embedded Controller) device in an integrated framework named EPCIS (Electronic Product Code for Information Systems). Among the advantages of the presented solution are: the ubiquitous access to the location of goods through its supply chain, the provision of evidence for import/export, the notification through alerts in case of exceptions (such as deviation from the planned trajectory, abnormal conditions for containers, etc.).","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117318162","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 increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.
{"title":"Automatic Ontology Identification for Reuse","authors":"M. Speretta, S. Gauch","doi":"10.1109/WI.2007.24","DOIUrl":"https://doi.org/10.1109/WI.2007.24","url":null,"abstract":"The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127379949","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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