The SphereSearch engine for unified ranked retrieval of heterogeneous XML and web documents
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Report on the DB/IR panel at SIGMOD 2005
ACM SIGMOD Record
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Efficiently linking text documents with relevant structured information
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
EntityRank: searching entities directly and holistically
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Exploiting web search engines to search structured databases
Proceedings of the 18th international conference on World wide web
SPARK: adapting keyword query to semantic search
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Web Semantics: Science, Services and Agents on the World Wide Web
GINO – a guided input natural language ontology editor
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A general framework for mining relations for the semantic web
Proceedings of the Ninth International Workshop on Information Integration on the Web
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The next wave in search technology will be driven by the identification, extraction, and exploitation of real-world entities represented in unstructured textual sources. Search systems will either let users express information needs naturally and analyze them more intelligently, or allow simple enhancements that add more user control on the search process. The data model will exploit graph structure where available, but not impose structure by fiat. First generation Web search, which uses graph information at the macroscopic level of inter-page hyperlinks, will be enhanced to use fine-grained graph models involving page regions, tables, sentences, phrases, and real-world-entities. New algorithms will combine probabilistic evidence from diverse features to produce responses that are not URLs or pages, but entities and their relationships, or explanations of how multiple entities are related.