Title: Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion,
Abstract: In this work several semantic approaches to concept-based query expansion and
reranking schemes are studied and compared with different ontology-based
expansion methods in web document search and retrieval. In particular, we focus
on concept-based query expansion schemes, where, in order to effectively
increase the precision of web document retrieval and to decrease the users
browsing time, the main goal is to quickly provide users with the most suitable
query expansion. Two key tasks for query expansion in web document retrieval
are to find the expansion candidates, as the closest concepts in web document
domain, and to rank the expanded queries properly. The approach we propose aims
at improving the expansion phase for better web document retrieval and
precision. The basic idea is to measure the distance between candidate concepts
using the PMING distance, a collaborative semantic proximity measure, i.e. a
measure which can be computed by using statistical results from web search
engine. Experiments show that the proposed technique can provide users with
more satisfying expansion results and improve the quality of web document
retrieval.