Inherited Feature-based Similarity Measure Based on Large Semantic Hierarchy and Large Text Corpus

We describe a similarity calculation model called IFSM (Inherited Feature Similarity Measure) between objects (words/concepts) based on their common and distinctive features. We propose an implementation method for obtaining features based on abstracted triples extracted fi'om a large text eorpus utilizing taxonomical knowledge. This model represents an integration of traditional methods, i.e,. relation b~used sin> itarity measure and distribution based similarity measure. An experiment, using our new concept abstraction method which we <'all the fiat probability grouping method, over 80,000 surface triples, shows that the abstraction level of 3000 is a good basis for feature description.