Experience of Developing a Meta-Semantic Search Engine

TL;DR

This paper discusses the development of SemanTelli, a meta-semantic search engine that integrates multiple semantic search engines, enhances ranking with snippet analysis, and supports image and news searches to improve relevance and diversity.

Contribution

It introduces enhancements to SemanTelli, including improved page ranking through snippet analysis and added support for image and news searches, advancing meta-semantic search technology.

Findings

Enhanced page ranking with snippet analysis improves relevance.

Integration of multiple semantic engines increases search diversity.

Support for images and news broadens search capabilities.

Abstract

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.