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Mining knowledge from natural language texts using fuzzy associated concept mapping

Mining knowledge from natural language texts using fuzzy associated concept mapping. Presenter : Kung, Chien-Hao Authors : W.M. Wang, C.F. Cheung, W.B. Lee, S.K. Kwok 2008,IPM. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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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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