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Mining knowledge from natural language texts using fuzzy associated concept mapping

This paper presents a novel approach to extracting and structuring knowledge from unstructured natural language texts. The primary motivation is to facilitate easy retrieval and processing of knowledge by computers, addressing the challenge posed by the informal structure of many documents. It outlines a framework that combines automatic and interactive processes, employing rule-based and case-based reasoning methodologies. The experiments demonstrate the effectiveness of this method in converting scientific texts into accessible formats, providing knowledge workers with new perspectives on their information.

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Mining knowledge from natural language texts using fuzzy associated concept mapping

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  1. Mining knowledge from natural language texts using fuzzy associated concept mapping Presenter : Kung, Chien-HaoAuthors : W.M. Wang, C.F. Cheung, W.B. Lee, S.K. Kwok2008,IPM

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Knowledge, for easy retrieval and processing by computers, should be represented in a formal, structured. • Unfortunately, knowledge presented in many documents has an informal, unstructured shape.

  4. Objectives • In order to provide advanced knowledge services, efficient ways are needed to access and extract knowledge from unstructured documents.

  5. Methodology-Framework • Automatic process • Interactive process

  6. Methodology • Automatic process

  7. Methodology • Automatic process • Rule-based reasoning (RBR) • Case-based reasoning (CBR).

  8. Methodology • Automatic process

  9. Methodology • Interactive process

  10. Experiment

  11. Experiment

  12. Conclusions • The method provides users to convert scientific and short texts into a structured format which can be easily processed by computer. • Moreover, the method provides knowledge workers to view their knowledge from another angle.

  13. Comments • Advantages • This paper supplies the rich information. • Applications • Concept mapping.

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