A smart web query engine for semantic retrieval of web data and its application to e-trading

Abstract

Vast amounts of data are available on the World Wide Web. However, the extraction and use of this data is difficult, since web data does not conform to any data organization standard. Search engines provide only primitive data query capabilities, and require a detailed syntactic specification to retrieve relevant data. This research proposes a Smart Web Query (SWQ) approach for the semantic retrieval of web data. The approach uses context and domain information to specify and formulate appropriate web queries and formats to search. The SWQ approach relies on context ontologies to discover relevant web pages. Unlike traditional ontologies, SWQ ontologies are structured on a set-theoretic model, which makes them more flexible, adaptive, extensible, and rapidly deployable. An SWQ engine is being developed to test the approach.