Automatic update of ontology concept hierarchy with new entity insertion and new concept generation based on semantic measurement

Ontology, as a representation of shared conceptualization for variety of specific domains, is the core of the semantic web. Concept hierarchy is one of the most popular backbones of ontology which organizes the concepts according to hyponymy relationships, and stores massive entities as the instances of the concepts. An open concept hierarchy, e.g., Wikipedia, always needs to be constantly updated by adding new entities and concepts. In this paper, we propose an automatic solution for ontology update by inserting new entities and generating new concepts for concept hierarchy. The method only requires very limited information of new entity, i.e., the attributes of each entity. The solution is based on a hybrid strategy synthesizing the benefits from the structure of the concept tree and the content of the attributes. The content of the attributes is used to measure the similarity between an entity and a concept. The structure of the concept tree is used to determine which concepts need to be measured. During similarity measurement, the solution also synthesizes the statistical and rule-based factors. The effectiveness of the proposed method is verified by the experiments extending the Chinese and English Wikipedia concept hierarchy with new entities and concepts.