A Domain Ontology Learning Approach Based on Soft-computing Techniques


Ontology is increasingly important in knowledge management and Semantic Web. The problem of it is that the construction of ontology is a time-consuming job and ontology engineers need to spend much time to maintain it. In this paper, we propose an incremental domain ontology learning method. This method can effectively extract new information from new domain documents to update the schema of domain ontology and make the knowledge base of domain ontology more complete based on a constructed domain ontology. First, we use schema of domain ontology to extract candidate instances. We also use genetic algorithm to learn the knowledge base of fuzzy inference. The three-layer parallel fuzzy inference mechanism is further applied to obtain new instances for ontology learning. In addition, new attributes, operations, and associations will be extracted based on episodes and morphological analysis to update the domain ontology.

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@inproceedings{Kao2003ADO, title={A Domain Ontology Learning Approach Based on Soft-computing Techniques}, author={Yuan-Fang Kao and Chang-Shing Lee and Yau-Hwang Kuo}, year={2003} }