DocumentCode :
3496820
Title :
Ontology-Based Fuzzy Semantic Clustering
Author :
Cheng, Yang
Author_Institution :
Commun. Eng. Dept., Changsha Univ., Changsha
Volume :
2
fYear :
2008
fDate :
11-13 Nov. 2008
Firstpage :
128
Lastpage :
133
Abstract :
Document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of documents into a small number of meaningful clusters. Most of the documents clustering methods were grounded in the bag of words representation to measure similarity, ignoring the semantic relationships between words that do not co-occur literally. A novel fuzzy semantic method that integrates ontology as background knowledge into the process of computing similarity between documents is proposed so as to improve the performance of documents clustering in terms of quality and efficiency. Ontology is represented as a graph-based model that reflects semantic relationship between concepts, with which a semantic similarity matrix of concepts that exploits semantic relation of the ontology is defined. Based on conceptual matrix a document can be represented to a semantic fuzzy set. Then similarity between documents is computed with fuzzy matching measure. The result of this process may make documents not similar with vector representation become similar. Maximal fuzzy spanning tree algorithm is used as a document-clustering algorithm. Finally the efficacy of our approach is demonstrated through relevant experiments.
Keywords :
document handling; fuzzy set theory; ontologies (artificial intelligence); browsing mechanism; conceptual matrix; document clustering algorithm; document similarity; fuzzy matching measure; graph-based model; intuitive navigation; maximal fuzzy spanning tree algorithm; ontology-based fuzzy semantic clustering; semantic fuzzy set; semantic similarity matrix; vector representation; words representation; Asia; Clustering algorithms; Clustering methods; Fuzzy sets; Information technology; Large scale integration; Navigation; Ontologies; Organizing; Unsupervised learning; Document Clustering; Fuzzy Semantic Clustering; Ontology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
Conference_Location :
Busan
Print_ISBN :
978-0-7695-3407-7
Type :
conf
DOI :
10.1109/ICCIT.2008.232
Filename :
4682226
Link To Document :
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