An index for efficient semantic full-text search
|An index for efficient semantic full-text search|
|Author(s)||Bast H., Buchhold B.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||Indexing, Query processing, Semantic full-text search (Extra: Context-sensitive, Full-text search, Index data structures, Keyword-based, Query construction, Real time, User need, Wikipedia, Indexing (of information), Knowledge management, Semantics, Query processing)|
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An index for efficient semantic full-text search is a 2013 conference paper written in English by Bast H., Buchhold B. and published in International Conference on Information and Knowledge Management, Proceedings.
In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms. Copyright is held by the owner/author(s).
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