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. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous Keywords concept ontology, supervised clustering, eRulemaking

• Type: Conference
• Year: 2008
• Where: DGO
• Authors: Hui Yang, Jamie Callan
• Keywords: Concept Ontology, DGO 2008, Education, Supervised Clustering, Supervised Clustering Algorithm