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Mining ontological knowledge from domain-specific text documents

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2 Author(s)
Xing Jiang ; Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Ah-Hwee Tan

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as text-to-onto, has shown that CRCTOL produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures.

Published in:

Data Mining, Fifth IEEE International Conference on

Date of Conference:

27-30 Nov. 2005