Mining knowledge from natural language texts using fuzzy associated concept mapping

1. Mining knowledge from natural language texts using fuzzy associated concept mapping Presenter : Wu, Jia-Hao Authors : W.M. Wang, C.F.Cheung,W.B. Lee, S.K. Kwork ˜ IPM (2008)

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments 2

3. Motivation • The amount of data of all kinds available electronically is increasing dramatically. • In the enterprises, about 80-98% of all data is consists of unstructured or semi-structured documents. • Knowledge presented in may documents has an informal, unstructured shape. • It has to be converted to a formal shape, with precisely defined syntax and semantics. (ex: document annotations)

4. Objective • Extracting the propositions in text so as to construct a concept map automatically. • The technique, Fuzzy Association Concept Mapping (FACM), is consists of a linguistic module and a recommendation module. • Provides a method which can be easily convert by computer. • Users can convert scientific and short texts into a structured format. • Provides knowledge workers with extra time to rethink their written text and to view their knowledge from another angle.

5. Objective (Cont.)

6. Methodology-FACM • The relations and concepts are generated from the document itself rather than retrieved from predefined ontologies. • It uses the syntactic structure of the sentences to find relations between the words. • An anaphoric resolution is applied based on rule-based reasoning (RBR) and case-based reasoning (CBR) for solving ambiguities arising during the syntactic analysis. • This enables a dynamic method of anaphoric resolution that is continually improved.

7. Methodology-Architecture of FACM. • Step 1.Input the Sentence. • Step 2.Parsing by POS tagger. • Step 3.Case encoding • Step 4.Produce the Solution.

8. Methodology-FACM’s Anaphora resolution • The similarity between the new case and old cases is calculated based on nearest neighbormatching. (1) (2)

9. Methodology-Proposition recommendation • The normalized frequency of concept i and concept j co-existing in the same or adjacent sentence is calculated:

10. Methodology-the relationship between concepts. • IF the normalized frequency of two concepts co-existing in the same sentence is High, THEN the relationship between the two concepts is High(0.7). • IF the normalized frequency of two concepts co-existing in the adjacent sentence is High, THEN the relationship between the two concepts is Medium(0.2). • The COG of fuzzy set A on the interval a1 to a2 with membership function uA is given: (c) (b) (a)

11. Experiments-SCI abstracts & News from CNET

12. Experiments-Results of algorithm evaluation

13. Conclusion • Provides an interactive way for concept map builders. • Rethink their concept maps. • Adapt and Refine the suggestions for completing the concept maps. • A human-like construction of concept maps can be achieved. • The highly accurate for use in extracting concepts from scientific and short texts such as abstract databases, news groups, emails, discussion forums, etc. • Future work • The system should be evaluated on bigger collections with more candidate users. • The evaluation of the interactive process of the framework is also an essential element. • Qualitative methods may be used to evaluate the effectiveness of the recommendation process.

14. Comments • Advantage • The convenient mining knowledge method. • Drawback • How to use the equation to produce the concept map. • Application • To analyze Abstract.