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Language-model-based ranking for queries on RDF-graphs
Abstract The success of knowledge-sharing communitiThe success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed. In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study. Copyright 2009 ACM.prehensive user study. Copyright 2009 ACM.
Abstractsub The success of knowledge-sharing communitiThe success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed. In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study. Copyright 2009 ACM.prehensive user study. Copyright 2009 ACM.
Bibtextype inproceedings  +
Doi 10.1145/1645953.1646078  +
Has author Elbassuoni S. + , Maya Ramanath + , Ralf Schenkel + , Sydow M. + , Gerhard Weikum +
Has extra keyword AS graph + , Augmented graph + , Automatic information extraction + , Graph model + , Knowledge repository + , Knowledge-sharing + , Kullback Leibler divergence + , Language model + , Model based approach + , Model-based + , Pattern search + , Query model + , Query semantics + , Ranking model + , RDF graph + , Search semantics + , User study + , Web data models + , Web sources + , Wikipedia + , Knowledge management + , Linguistics + , Semantics + , Query languages +
Has keyword Entity + , Language + , Model + , Ranking + , RDF + , Relationship + , Search + , Semantics +
Isbn 9781605585123  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 977–986  +
Published in International Conference on Information and Knowledge Management, Proceedings +
Title Language-model-based ranking for queries on RDF-graphs +
Type conference paper  +
Year 2009 +
Creation dateThis property is a special property in this wiki. 7 November 2014 23:21:28  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 23:21:28  +
DateThis property is a special property in this wiki. 2009  +
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