DocumentCode :
2385535
Title :
Granular Computing and Web Processing: Representing Documents in Polyhedron
Author :
Lin, T.Y.
Author_Institution :
San Jose State Univ., San Jose
fYear :
2007
fDate :
2-4 Nov. 2007
Firstpage :
6
Lastpage :
6
Abstract :
Summary form only given. Granular computing (GrC) is a pragmatic approach to data and knowledge engineering. Roughly, it is a methodology that involves elements (data) and subsets (knowledge). We will illustrate the idea via a web application. In traditional web processing, we often represent a document by a set of keywords. In GrC, let us call it granular representation, we include the knowledge. More precisely, a document is not only represented by its keywords, but also by its granules. Here by a granule we mean a keyword association, which is a set of frequent co-occurring near by keywords. For a trivial example, the association, "Wall street," as a financial concept, is in the granular representation. The granular representation has an interesting geometric interpretation. We can regard the set of keywords as a set of vertices, and the set of keyword associations as a set of simplexes. Interestingly in such a translation, the a priori principle is converted into the closed condition of simplical complexes. In other words, the collection of keywords and keyword associations is an abstract simplicial complex in algebraic topology.
Keywords :
Internet; combinatorial mathematics; document handling; Web processing; algebraic topology; document representation; geometric interpretation; granular computing; keyword association; knowledge engineering; simplicial complex; Chapters; Computer Society; Computer science; Data mining; Data security; Data warehouses; Information retrieval; Knowledge engineering; Silicon; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
Type :
conf
DOI :
10.1109/GrC.2007.159
Filename :
4403055
Link To Document :
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