NAGA: Searching and ranking knowledge

Gjergji Kasneci, Fabian M. Suchanek, Georgiana Ifrim, Maya Ramanath, Gerhard Weikum

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

166 Zitate (Scopus)

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, located In, 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 such as 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.

OriginalspracheEnglisch
TitelProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Seiten953-962
Seitenumfang10
DOIs
PublikationsstatusVeröffentlicht - 2008
Extern publiziertJa
Veranstaltung2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexiko
Dauer: 7 Apr. 200812 Apr. 2008

Publikationsreihe

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Konferenz

Konferenz2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Land/GebietMexiko
OrtCancun
Zeitraum7/04/0812/04/08

Fingerprint

Untersuchen Sie die Forschungsthemen von „NAGA: Searching and ranking knowledge“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren