A Knowledge-Based Multi-Agent Approach for Initial Query Refinement in Information Retrieval
Query refinement is one of the main approaches for overcoming
natural language lexical ambiguity and improving the quality of
search results in Information Retrieval. In this paper we propose
a knowledge-rich personalized approach for iterative query reformulation
before sending it to search engines and entropybased
approach for refinement quality estimation. The underling
hypothesis is that the query reformulation entropy is a valuable
characteristic of the refinement quality. We use multi-agent architecture
to implement our approach. Experimental results confirm
that there is a trend for significant improvement in the quality
of search results for large values of entropy.
Keywords: semantic search, multi-agent system, query refinement,
query refinement entropy
Download Full-Text
