Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Mining knowledge from natural language texts using fuzzy associated concept mapping Presenter : Wu,

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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) ˜

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 22 Outline Motivation Objective Methodology Experiments Conclusion Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Objective (Cont.)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Methodology-FACM’s Anaphora resolution The similarity between the new case and old cases is calculated based on nearest neighbor matching. (1) (2)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Methodology-Proposition recommendation The normalized frequency of concept i and concept j co- existing in the same or adjacent sentence is calculated:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Methodology-the relationship between concepts. (a) (b) (c) 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 a 1 to a 2 with membership function u A is given:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Experiments-SCI abstracts & News from CNET

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Experiments-Results of algorithm evaluation

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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, s, 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Comments Advantage  The convenient mining knowledge method. Drawback  How to use the equation to produce the concept map. Application  To analyze Abstract.