Automated Chinese domain ontology construction from text documents

Authors:
Yu Zheng;Wenxiang Dou;Gengfeng Wu;Xin Li
Affiliations:
School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China;School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China;School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China;School of Electromechanical Engineering and Automation, Shanghai University, Shanghai, P. R. China
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

Citing 7
Cited 0

Mining generalized association rules

Future Generation Computer Systems - Special double issue on data mining
Using text processing techniques to automatically enrich a domain ontology

Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Ontology Learning for the Semantic Web

IEEE Intelligent Systems
Ontology Specification Languages for the Semantic Web

IEEE Intelligent Systems
Automatic Ontology-Based Knowledge Extraction from Web Documents

IEEE Intelligent Systems
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ontology as an important knowledge representation tool is widely used in many fields. Constructing domain ontology is a lengthy, costly task. Rapid, accurate construction of ontology has thus become an important topic. In this paper, a method that automates construction of the ontology is proposed. The method integrates text analysis, TF/IDF calculation, association rules extraction, pattern rules matching and RDF technologies. The ontology construction method does not require expenditure of time to select keywords and to define the relations by human edit or expert assistance. The method facilitates user understanding of the content of data and its relevancy, and is able to suggest content that is highly relevant. Experimental results show that the proposed approach can effectively construct Chinese domain ontology from text documents.