Semantic Similarity in a Taxonomy: An Information-Based Measure and its
  Application to Problems of Ambiguity in Natural Language
  This article presents a measure of semantic similarity in an IS-A taxonomy
based on the notion of shared information content. Experimental evaluation
against a benchmark set of human similarity judgments demonstrates that the
measure performs better than the traditional edge-counting approach. The
article presents algorithms that take advantage of taxonomic similarity in
resolving syntactic and semantic ambiguity, along with experimental results
demonstrating their effectiveness.
