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Domain ontology construction based on semantic relation information of terminology
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An ontology is an explicit specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This thesis suggests a method of constructing domain ontology semi-automatically using terminology processing and applies the method to document retrieval. In order to construct ontology, we propose an algorithm that classifies the patterns of nouns and suffices which compose terminology, in domain texts, extracts terminology, and build a hierarchical structure. The experiment used documents related to pharmacy domain. As singleton terms combined with specific nouns or suffices were identified, 2,864 sub-concepts were added and the algorithm showed accuracy of 92.57% was achieved on the average. In case of multi-word terms, 574 concepts were added and the average accuracy was 66.64%. Constructed ontology, which forms natural groups of senses centering on specific nouns or suffices composing the terminology with semantic information, can be utilized in approaching the knowledge of special areas such as document retrieval. According to the result of document retrieval based on the constructed ontology, the system improved precision by 4.97% compared to keyword-based document retrieval with traditional TFㆍIDF method. Also recall was improved by 0.78%. As stated above, Ontology constructed by analyzing texts in a specific domain adds concepts and relationships automatically. As a result, it maintains richer information, can answer various queries, and enhance the accuracy of retrieval. This suggests that concepts and rules defined in ontology may be used as the base of inference to improve the performance of retrieval. Subsequent research will be focused on how to apply the proposed method of ontology construction to general domain.