The Experts below are selected from a list of 33339 Experts worldwide ranked by ideXlab platform
David Vallet - One of the best experts on this subject based on the ideXlab platform.
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An adaptation of the vector-space model for ontology-based information retrieval
IEEE Transactions on Knowledge and Data Engineering, 2007Co-Authors: Pablo Castells, Miriam Fernandez, David ValletAbstract:Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
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an ontology based information retrieval model
European Semantic Web Conference, 2005Co-Authors: David Vallet, Miriam Fernandez, Pablo CastellsAbstract:Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.
Pablo Castells - One of the best experts on this subject based on the ideXlab platform.
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An adaptation of the vector-space model for ontology-based information retrieval
IEEE Transactions on Knowledge and Data Engineering, 2007Co-Authors: Pablo Castells, Miriam Fernandez, David ValletAbstract:Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
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an ontology based information retrieval model
European Semantic Web Conference, 2005Co-Authors: David Vallet, Miriam Fernandez, Pablo CastellsAbstract:Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.
Panagiotis G. Ipeirotis - One of the best experts on this subject based on the ideXlab platform.
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examining the impact of Ranking on consumer behavior and search engine revenue
Management Science, 2014Co-Authors: Anindya Ghose, Panagiotis G. IpeirotisAbstract:In this paper, we study the effects of three different kinds of search engine Rankings on consumer behavior and search engine revenues: direct Ranking effect, interaction effect between Ranking and product ratings, and personalized Ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: 1 A consumer-utility-based Ranking mechanism can lead to a significant increase in overall search engine revenue. 2 Significant interplay occurs between search engine Ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their Ranking Algorithms. 3 Our randomized experiments also reveal that an “active” personalized Ranking system wherein users can interact with and customize the Ranking Algorithm leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized Ranking system wherein users cannot interact with the Ranking Algorithm. This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized Ranking systems by default. Overall, our study unravels the economic impact of Ranking and its interaction with social media on product search engines. This paper was accepted by Lorin Hitt, information systems.
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Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue
Management Science, 2014Co-Authors: Anindya Ghose, Panagiotis G. Ipeirotis, Beibei LiAbstract:In this paper, we study the effects of three different kinds of search engine Rankings on consumer behavior and search engine revenues: direct Ranking effect, interaction effect between Ranking and product ratings, and personalized Ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based Ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine Ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their Ranking Algorithms. (3) Our randomized experiments also reveal that an “active” personalized Ranking system (wherein users can interact with and customize the Ranking Algorithm) leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized Ranking system (wherein users cannot interact with the Ranking Algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized Ranking systems by default. Overall, our study unravels the economic impact of Ranking and its interaction with social media on product search engines.
Miriam Fernandez - One of the best experts on this subject based on the ideXlab platform.
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An adaptation of the vector-space model for ontology-based information retrieval
IEEE Transactions on Knowledge and Data Engineering, 2007Co-Authors: Pablo Castells, Miriam Fernandez, David ValletAbstract:Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
-
an ontology based information retrieval model
European Semantic Web Conference, 2005Co-Authors: David Vallet, Miriam Fernandez, Pablo CastellsAbstract:Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting Algorithm, and a Ranking Algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.
Anindya Ghose - One of the best experts on this subject based on the ideXlab platform.
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examining the impact of Ranking on consumer behavior and search engine revenue
Management Science, 2014Co-Authors: Anindya Ghose, Panagiotis G. IpeirotisAbstract:In this paper, we study the effects of three different kinds of search engine Rankings on consumer behavior and search engine revenues: direct Ranking effect, interaction effect between Ranking and product ratings, and personalized Ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: 1 A consumer-utility-based Ranking mechanism can lead to a significant increase in overall search engine revenue. 2 Significant interplay occurs between search engine Ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their Ranking Algorithms. 3 Our randomized experiments also reveal that an “active” personalized Ranking system wherein users can interact with and customize the Ranking Algorithm leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized Ranking system wherein users cannot interact with the Ranking Algorithm. This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized Ranking systems by default. Overall, our study unravels the economic impact of Ranking and its interaction with social media on product search engines. This paper was accepted by Lorin Hitt, information systems.
-
Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue
Management Science, 2014Co-Authors: Anindya Ghose, Panagiotis G. Ipeirotis, Beibei LiAbstract:In this paper, we study the effects of three different kinds of search engine Rankings on consumer behavior and search engine revenues: direct Ranking effect, interaction effect between Ranking and product ratings, and personalized Ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based Ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine Ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their Ranking Algorithms. (3) Our randomized experiments also reveal that an “active” personalized Ranking system (wherein users can interact with and customize the Ranking Algorithm) leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized Ranking system (wherein users cannot interact with the Ranking Algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized Ranking systems by default. Overall, our study unravels the economic impact of Ranking and its interaction with social media on product search engines.
