Mathematical Model of Semantic Look - An Efficient Context Driven Search Engine
This addresses the problem of inefficient search results for web users, but it appears incremental as it builds on semantic web concepts and compares to a specific existing method.
The authors tackled the problem of search engines returning unrelated results by ignoring query context, proposing Semantic Look, a context-driven search engine that processes semantic information in web pages. They showed it is more than twice as fast than OntoLook, an existing algorithm.
The WorldWideWeb (WWW) is a huge conservatory of web pages. Search Engines are key applications that fetch web pages for the user query. In the current generation web architecture, search engines treat keywords provided by the user as isolated keywords without considering the context of the user query. This results in a lot of unrelated pages or links being displayed to the user. Semantic Web is based on the current web with a revised framework to display a more precise result set as response to a user query. The current web pages need to be annotated by finding relevant meta data to be added to each of them, so that they become useful to Semantic Web search engines. Semantic Look explores the context of user query by processing the Semantic information recorded in the web pages. It is compared with an existing algorithm called OntoLook and it is shown that Semantic Look is a better optimized search engine by being more than twice as fast as OntoLook.