Mining ontological knowledge from domain-specific text documents

• Main Authors: JIANG, Xing, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6666; https://ink.library.smu.edu.sg/context/sis_research/article/7669/viewcontent/Ontology_ICDM05.pdf
• Institution: Singapore Management University

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.