Type less, find more: fast autocompletion search with a succinct index
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ESTER: efficient search on text, entities, and relations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
RDF-3X: a RISC-style engine for RDF
Proceedings of the VLDB Endowment
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Index compression using 64-bit words
Software—Practice & Experience
Ad-hoc object retrieval in the web of data
Proceedings of the 19th international conference on World wide web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Modern Information Retrieval
Effective and efficient entity search in RDF data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Lightweight integration of IR and DB for scalable hybrid search with integrated ranking support
Web Semantics: Science, Services and Agents on the World Wide Web
GoNTogle: a tool for semantic annotation and search
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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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.