UNSUPERVISED ONTOLOGY POPULATION USING LATENT SEMANTIC ANALYSIS
Theerayut Thongkrau, Pattarachai Lalitrojwong · 2010
A large ontology such as lexical ontology is useful as the basic knowledge base in artificial intelligence and computational linguistics application. However, it is insufficient to recognize only existing instances for each concept. Adding new instances into the lexical ontology will expand knowledge in the system. In this paper, we propose an efficient unsupervised ontology population system that classifies new instances into a corresponding lexical ontology concept. Compared to previous related works, it does not require manual preprocessing to prepare training data. In terms of processing time, it does not need to search for many concepts in the lexical ontology. Our system employs latent semantic analysis together with context voting to find the appropriate concept of the instance. In sum, the system achieves higher accuracy when the lexical ontology contains a lot of concepts, which generally occurs in practical problems.