Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
A Feature Selection Framework for Text Filtering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On Using Partial Supervision for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks
Proceedings of the 2005 ACM symposium on Applied computing
A Taxonomy Learning Method and Its Application to Characterize a Scientific Web Community
IEEE Transactions on Knowledge and Data Engineering
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
Text-based domain ontology building using tf-idf and metric clusters techniques
The Knowledge Engineering Review
Text Clustering with Feature Selection by Using Statistical Data
IEEE Transactions on Knowledge and Data Engineering
Improving Automatic Text Classification by Integrated Feature Analysis
IEICE - Transactions on Information and Systems
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This paper presents a Collaborative Ontology Learning Approach for the implementation of an Ontology-based Web Content Management System (OWCMS). The proposal system integrates two supervised learning approach - Content-based Learning and User-based Learning Approach. The Content-based Learning Approach applies text mining methods to extract ontology concepts, and to build an Ontology Graph (OG) through the automatic learning of web documents. The User-based Learning Approach applies features analysis methods to extract the subset of the Ontology Graphs, in order to build a personalized ontology by using intelligent agent approach to capture user reading habit and preference through their semantic navigation and search over the ontology-based web content. This system combines the two methods to create collaborative ontology learning through an ontology matching and refinement process on the ontology created from content-based learning and user-based learning. The proposed method improves the validness of the classical ontology learning outcome by user-based learning refinement and validation.