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NAGA: Harvesting, searching and ranking knowledge
Abstract The presence of encyclopedic Web sources, The presence of encyclopedic Web sources, such as Wikipedia, the Internet Movie Database (IMDB), World Factbook, etc. calls for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for explicit knowledge needs to consider inherent semantic structures involving entities and relationships. In this demonstration proposal, we describe a semantic search system named NAGA. NAGA operates on a knowledge graph, which contains millions of entities and relationships derived from various encyclopedic Web sources, such as the ones above. NAGA's graph-based query language is geared towards expressing queries with additional semantic information. Its scoring model is based on the principles of generative language models, and formalizes several desiderata such as confidence, informativeness and compactness of answers. We propose a demonstration of NAGA which will allow users to browse the knowledge base through a user interface, enter queries in NAGA's query language and tune the ranking parameters to test various ranking aspects.arameters to test various ranking aspects.
Abstractsub The presence of encyclopedic Web sources, The presence of encyclopedic Web sources, such as Wikipedia, the Internet Movie Database (IMDB), World Factbook, etc. calls for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for explicit knowledge needs to consider inherent semantic structures involving entities and relationships. In this demonstration proposal, we describe a semantic search system named NAGA. NAGA operates on a knowledge graph, which contains millions of entities and relationships derived from various encyclopedic Web sources, such as the ones above. NAGA's graph-based query language is geared towards expressing queries with additional semantic information. Its scoring model is based on the principles of generative language models, and formalizes several desiderata such as confidence, informativeness and compactness of answers. We propose a demonstration of NAGA which will allow users to browse the knowledge base through a user interface, enter queries in NAGA's query language and tune the ranking parameters to test various ranking aspects.arameters to test various ranking aspects.
Bibtextype inproceedings  +
Doi 10.1145/1376616.1376756  +
Has author Gjergji Kasneci + , Suchanek F.M. + , Ifrim G. + , Elbassuoni S. + , Maya Ramanath + , Gerhard Weikum +
Has extra keyword Entities + , Explicit knowledges + , Internet movie databases + , Knowledge base + , Language models + , Ranking + , Relationships + , Scoring models + , Searching and rankings + , Semantic informations + , Semantic search + , Semantic searches + , Semantic structures + , Web sources + , Wikipedia + , Computational linguistics + , Database systems + , Information theory + , Knowledge based systems + , Linguistics + , Search engine + , Semantics + , User interfaces + , Query languages +
Has keyword Entities + , Ranking + , Relationships + , Semantic search + , User interface +
Isbn 9781605581026  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1285–1287  +
Published in Proceedings of the ACM SIGMOD International Conference on Management of Data +
Title NAGA: Harvesting, searching and ranking knowledge +
Type conference paper  +
Year 2008 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:47:21  +
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. 8 November 2014 05:47:21  +
DateThis property is a special property in this wiki. 2008  +
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