Semplore: An IR approach to scalable hybrid query of Semantic Web data
|Semplore: An IR approach to scalable hybrid query of Semantic Web data|
|Author(s)||Zhang L., Liu Q., Zhang J., Wang H., Pan Y., Yu Y.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Combines, Computer software, Data structures, Industrial economics, Information retrieval, Information services, Information theory, Search engines, Semantic Web, Semantics, Index structures, IR engines, Keyword searches, Product information, Promising approach, Semantic Web data, Structured data, Structured queries, Textual information, Web searches, Wikipedia, World Wide Web)|
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Semplore: An IR approach to scalable hybrid query of Semantic Web data is a 2007 conference paper written in English by Zhang L., Liu Q., Zhang J., Wang H., Pan Y., Yu Y. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.
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