Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
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
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This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model - ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately.