Exploiting Content and Social Information for Ontology-based Hierarchical Classification
Date Issued
2009
Date
2009
Author(s)
Liu, Chien-Pang
Abstract
The objective of this thesis is to develop an unsupervised hierarchical classification system in which a given proper noun is classified into an appropriate category of a designated ontology. Different from other approaches, our methods exploit both content and social information to show that combining weaker similarity measures could produce a stronger one. To take the hierarchical information into account, we also propose a novel path-based classification strategy.n our work, similarities of proper nouns and categories are captured using three different models: a content-based model using pointwise mutual information; a static social model based on social similarity, and a dynamic social model through exploiting the PageRank algorithm on a social network. Our hierarchical classification algorithms exploit both the ontology structure and similarity measures to identify the category of a given proper noun. The experimental results on ACM Computing Classification System show that our proposed classification algorithm, when used combined similarity measure, can improve significantly the effectiveness of the proper noun classification.
Subjects
ontology
hierarchical classification
similarity measure
taxonomic similarity
Type
thesis
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