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NAGA: Harvesting, Searching and Ranking Knowledge

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44738

Kasneci,  Gjergji
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45572

Suchanek,  Fabian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44668

Ifrim,  Georgiana
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44375

Elbassuoni,  Shady
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45248

Ramanath,  Maya
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Kasneci, G., Suchanek, F., Ifrim, G., Elbassuoni, S., Ramanath, M., & Weikum, G. (2008). NAGA: Harvesting, Searching and Ranking Knowledge. In D. Shasha, & J. T. L. Wang (Eds.), Proceedings of the ACM SIGMOD 2008 International Conference on Management of Data (pp. 1285-1288). New York, NY: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1C5E-E
Zusammenfassung
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.