Abstract
The key task for the web, namely, web searches, is evolving towards some novel form of semantic web search. In fact, most information retrieval systems are based on static vectors representations. Two major difficulties when a researcher uses current information retrieval systems are how to filter out irrelevant documents, and how to discover latest or more significant documents. Recently a very promising approach to semantic web search is based on combining standard web pages and search queries with ontological background knowledge. In this perspective we will describe a model to hold a document’s noise, and incompleteness. For this, we merge a syntactic keyword search with purely semantic search based domain ontology and a multi-agent system to solve such distributed problems. Then we perform a ranking algorithm on returned documents, and we propose a new semantic similarity measure between concepts based on the WordNet taxonomy structure.
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