The Extension of Domain Ontology Based on Text Clustering

As the base of Semantic Web of Things(SWOT), ontology construction and ontology automatic extension has become a research hot-spot in recent years. However, the expansion of ontology still requires manual work by domain experts, and it is inefficient and costly. This paper summarizes the related concepts and methods of ontology construction and ontology extension, proposing an automatic ontology extension method based on supervised learning and text clustering. This method uses the K-means clustering algorithm to separate the domain knowledge and to guide the creation of training set for Naive Bayes classifier. Words in candidate set will be added to the target ontology, at the same time, noise words will be added to the stop-word dictionary. The feedback mechanism of this method is designed to promote the architecture of ontology's accuracy, and ultimately it will extend ontology semi-automatically.