Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25919
Title: Mining knowledge from natural language texts using fuzzy associated concept mapping
Authors: Wang, WM
Cheung, CF 
Lee, WB 
Kwok, SK
Keywords: Concept mapping
Fuzzy set theory
Information retrieval
Knowledge management
Knowledge mining
Natural language processing
Issue Date: 2008
Publisher: Pergamon Press
Source: Information processing and management, 2008, v. 44, no. 5, p. 1707-1719 How to cite?
Journal: Information processing and management 
Abstract: Natural Language Processing (NLP) techniques have been successfully used to automatically extract information from unstructured text through a detailed analysis of their content, often to satisfy particular information needs. In this paper, an automatic concept map construction technique, Fuzzy Association Concept Mapping (FACM), is proposed for the conversion of abstracted short texts into concept maps. The approach consists of a linguistic module and a recommendation module. The linguistic module is a text mining method that does not require the use to have any prior knowledge about using NLP techniques. It incorporates rule-based reasoning (RBR) and case based reasoning (CBR) for anaphoric resolution. It aims at extracting the propositions in text so as to construct a concept map automatically. The recommendation module is arrived at by adopting fuzzy set theories. It is an interactive process which provides suggestions of propositions for further human refinement of the automatically generated concept maps. The suggested propositions are relationships among the concepts which are not explicitly found in the paragraphs. This technique helps to stimulate individual reflection and generate new knowledge. Evaluation was carried out by using the Science Citation Index (SCI) abstract database and CNET News as test data, which are well known databases and the quality of the text is assured. Experimental results show that the automatically generated concept maps conform to the outputs generated manually by domain experts, since the degree of difference between them is proportionally small. The method provides users with the ability to convert scientific and short texts into a structured format which can be easily processed by computer. Moreover, it provides knowledge workers with extra time to re-think their written text and to view their knowledge from another angle.
URI: http://hdl.handle.net/10397/25919
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2008.05.002
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