Experience of Developing a Meta-Semantic Search Engine
This work addresses the need for more relevant search results for users by improving a meta-semantic search engine, but it appears incremental as it builds on an existing system.
The paper tackles the problem of keyword-based web search by proposing enhancements to SemanTelli, a meta-semantic search engine, with an improved page ranking algorithm and support for image and news search, though no concrete performance numbers are provided.
Thinking of todays web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web. Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages. On other hand, semantic search engines provide efficient and relevant results as the semantic web manages information with well defined meaning using ontology. A Meta-Search engine is a search tool that forwards users query to several existing search engines and provides combined results by using their own page ranking algorithm. SemanTelli is a meta semantic search engine that fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot through intelligent agents. This paper proposes enhancement of SemanTelli with improved snippet analysis based page ranking algorithm and support for image and news search.