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Conference Paper

ESTER: efficient search on Text, Entities, and Relations

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44076

Bast,  Holger
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44244

Chitea,  Alexandru
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45715

Weber,  Ingmar
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Citation

Bast, H., Chitea, A., Suchanek, F. M., & Weber, I. (2007). ESTER: efficient search on Text, Entities, and Relations. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. P. de Vries (Eds.), SIGIR'07: 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 671-678). New York, NY, USA: ACM.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1F17-3
Abstract
We present ESTER, a modular and highly efficient system for combined full-text and ontology search. ESTER builds on a query engine that supports two basic operations: prefix search and join. Both of these can be implemented very efficiently with a compact index, yet in combination provide powerful querying capabilities. We show how ESTER can answer basic SPARQL graph-pattern queries on the ontology by reducing them to a small number of these two basic operations. ESTER further supports a natural blend of such semantic queries with ordinary full-text queries. Moreover, the prefix search operation allows for a fully interactive and proactive user interface, which after every keystroke suggests to the user possible semantic interpretations of his or her query, and speculatively executes the most likely of these interpretations. As a proof of concept, we applied ESTER to the English Wikipedia, which contains about 3 million documents, combined with the recent YAGO ontology, which contains about 2.5 million facts. For a variety of complex queries, ESTER achieves worst-case query processing times of a fraction of a second, on a single machine, with an index size of about 4 GB.