Mining knowledge from natural language texts using fuzzy associated concept mapping

Knowledge Management Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Kowloon, Hong Kong
Information Processing & Management (Impact Factor: 1.27). 09/2008; 44(5):1707-1719. DOI: 10.1016/j.ipm.2008.05.002
Source: DBLP


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.

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Available from: W.M. Wang, Jun 22, 2015
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    • "Bai and Chen simplified and improved the latter method in adaptive way [15]. Wang based on the FST has developed another method for the non-explicit links between concepts [16]. These methods mentioned above do not take into account the possibility of combining the concept maps predefined by experts of field and the automatic generation of these concepts map from the evaluation results obtained by learners during of process learning. "
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    ABSTRACT: In recent years, adaptive learning systems rely increasingly on concept maps to customize the educational logic developed in their courses. Most approaches do not take into account the possibility of combining the concept maps predefined by experts of field and those developed automatically using the Fuzzy Sets Theory. In this article, we present a hybrid approach using on the one hand the feedback from experts of domain to select, prioritize relevant concepts and create prerequisite relationships to get the initial concept map, on the other hand we use the fuzzy logic to measure relevance degree of all relationships existing in this concept map, these links are considered as fuzzy relationships. With this approach we got two types of prerequisite relationships between concepts, the first type can be classified as relationships correctly established by the expert. These relationships must be kept in the final concept map. The second type can be considered as relationships incorrectly established by the expert, because the concepts involved in these relationships are independent, in this case these relations must be deleted or substituted with the inverse of the original relations, or because the items used in evaluations of these concepts are inappropriate and must be reviewed.
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    • "The problem with linguistic techniques is that they are limited to a specific language, and linguistic resources must exist for used language. The majority of linguistic tools and methods are based on the English language, and researchers mostly use them in CMM [14], [15], [17], [18], [23]–[27], [32]. A number of them use the WordNet for lemmatization, POS tagging and terms disambiguation. "
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