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Conference Paper

NAGA: Searching and Ranking Knowledge

MPS-Authors
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/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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Citation

Kasneci, G., Suchanek, F., Ifrim, G., Ramanath, M., & Weikum, G. (2008). NAGA: Searching and Ranking Knowledge. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (ICDE'08) (pp. 953-962). Piscataway, NJ: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1C60-6
Abstract
The Web has the potential to become the world’s largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for knowledge rather than Web pages needs to consider inherent semantic structures like entities (person, organization, etc.) and relationships (isA, locatedIn, etc.). In this paper, we propose NAGA, a new semantic search engine. NAGA builds on a knowledge base, which is organized as a graph with typed edges, and consists of millions of entities and relationships extracted from Web-based corpora. A graph-based query language enables the formulation of queries with additional semantic information. We introduce a novel scoring model, based on the principles of generative language models, which formalizes several notions like confidence, informativeness and compactness and uses them to rank query results. We demonstrate NAGA’s superior result quality over state-of-the-art search engines and question answering systems.