Mining ontological knowledge from domain-specific text documents
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 p...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2005
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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