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Poo, D.C.C.; Khoo, C.: Subject searching in online catalog systems (1997)
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- Source
- Encyclopedia of library and information science. Vol.60, [=Suppl.23]
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Wang, Z.; Chaudhry, A.S.; Khoo, C.: Support from bibliographic tools to build an organizational taxonomy for navigation : use of a general classification scheme and domain thesauri (2010)
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- Abstract
- A study was conducted to investigate the capability of a general classification scheme and domain thesauri to support the construction of an organizational taxonomy to be used for navigation, and to develop steps and guidelines for constructing the hierarchical structure and categories. The study was conducted in the context of a graduate department in information studies in Singapore that offers Master's and PhD programs in information studies, information systems, and knowledge management. An organizational taxonomy, called Information Studies Taxonomy, was built for learning, teaching and research tasks of the department using the Dewey Decimal Classification and three domain thesauri (ASIS&T, LISA, and ERIC). The support and difficulties of using the general classification scheme and domain thesauri were identified in the taxonomy development process. Steps and guidelines for constructing the hierarchical structure and categories were developed based on problems encountered in using the sources.
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Khoo, C.; Chan, S.; Niu, Y.: ¬The many facets of the cause-effect relation (2002)
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- Series
- Information science and knowledge management; vol.3
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Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002)
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- Series
- Information science and knowledge management; vol.3
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Lee, C.-H.; Khoo, C.; Na, J.-C.: Automatic identification of treatment relations for medical ontology learning : an exploratory study (2004)
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- Abstract
- This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) semantic net. The study found that semantic relations between concepts could be inferred 68% of the time, although the method often could not distinguish between a few possible relation types. Our current research focuses an the use of natural language processing techniques to improve the identification of semantic relations. In particular, we explore both a semi-automatic and manual construction of linguistic patterns for identifying treatment relations in medical abstracts in the domain of colon cancer treatment. Association rule mining was applied to sample sentences containing both a disease concept and a reference to drug, to identify frequently occurring word pattems to see if these pattems could be used to identify treatment relations in sentences. This did not yield many useful patterns, suggesting that statistical association measures have to be complemented with syntactic and semantic constraints to identify useful patterns. In the second part of the study, linguistic patterns were manually constructed based an the same sentences. This yielded promising results. Work is ongoing to improve the manually constructed pattems as well as to identify the syntactic and semantic constraints that can be used to improve the automatic construction of linguistic patterns.
