  Text search based on lexical matching of keywords is not satisfactory due to
polysemous and synonymous words. Semantic search that exploits word meanings,
in general, improves search performance. In this paper, we survey WordNet-based
information retrieval systems, which employ a word sense disambiguation method
to process queries and documents. The problem is that in many cases a word has
more than one possible direct sense, and picking only one of them may give a
wrong sense for the word. Moreover, the previous systems use only word forms to
represent word senses and their hypernyms. We propose a novel approach that
uses the most specific common hypernym of the remaining undisambiguated
multi-senses of a word, as well as combined WordNet features to represent word
meanings. Experiments on a benchmark dataset show that, in terms of the MAP
measure, our search engine is 17.7% better than the lexical search, and at
least 9.4% better than all surveyed search systems using WordNet.
  Keywords Ontology, word sense disambiguation, semantic annotation, semantic
search.
