A heuristic-based methodology for semantic augmentation of user queries on the web

As the World Wide Web continues to grow, so does the need for effective approaches to processing users' queries that retrieve the most relevant information. Most search engines provide the user with many web pages, but at varying levels of relevancy. The Semantic Web has been proposed to retrieve and use more semantic information from the web. However, the capture and processing of semantic information is a difficult task because of the well-known problems that machines have with processing semantics. This research proposes a heuristic-based methodology for building context aware web queries. The methodology expands a user's query to identify possible word senses and then makes the query more relevant by restricting it using relevant information from the WordNet lexicon and the DARPA DAML library of domain ontologies. The methodology is implemented in a prototype. Initial testing of the prototype and comparison to results obtained from Google show that this heuristic based approach to processing queries can provide more relevant results to users, especially when query terms are ambiguous and/or when the methodology's heuristics are invoked.