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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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: JIANG, Xing, TAN, Ah-hwee
التنسيق: text
اللغة:English
منشور في: Institutional Knowledge at Singapore Management University 2005
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الوصول للمادة أونلاين: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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spelling sg-smu-ink.sis_research-76692022-01-13T09:34:24Z Mining ontological knowledge from domain-specific text documents JIANG, Xing TAN, Ah-hwee 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. 2005-11-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6666 info:doi/10.1109/ICDM.2005.97 https://ink.library.smu.edu.sg/context/sis_research/article/7669/viewcontent/Ontology_ICDM05.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
JIANG, Xing
TAN, Ah-hwee
Mining ontological knowledge from domain-specific text documents
description 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.
format text
author JIANG, Xing
TAN, Ah-hwee
author_facet JIANG, Xing
TAN, Ah-hwee
author_sort JIANG, Xing
title Mining ontological knowledge from domain-specific text documents
title_short Mining ontological knowledge from domain-specific text documents
title_full Mining ontological knowledge from domain-specific text documents
title_fullStr Mining ontological knowledge from domain-specific text documents
title_full_unstemmed Mining ontological knowledge from domain-specific text documents
title_sort mining ontological knowledge from domain-specific text documents
publisher Institutional Knowledge at Singapore Management University
publishDate 2005
url 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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