Ontology generation for large email collections

This paper presents a new approach to identifying concepts expressed in a collection of email messages, and organizing them into an ontology or taxonomy for browsing. It incorporates techniques from text mining, information retrieval, natural language processing and machine learning to generate a concept ontology. Nominal N-gram mining is used to identify candidate concepts. Wordnet and surface text pattern matching are used to identify relationships among the concepts. A supervised clustering algorithm is then used to further cluster the concepts. The experiments show that the approach is effective.

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