An automatic ontology population with a machine learning technique from semi-structured documents

Abstract

The manual design of an ontology usually defines the concepts for the domain, but the individual instances of the concepts are often missing though they are important in using the ontology as a knowledge base. This is due to high cost of the manual construction of individuals. In order to tackle this problem, this paper proposes an automatic method for ontology population. The knowledge source for ontology population used in this paper is the Web tables of which structure is relatively well organized. Since a Web table can be analyzed into a parse tree, the most appropriate concept within the ontology for a given Web table is determined by a kernel method, so-called a parse tree kernel. Then, the table is populated as an individual of the concept. According to the experimental results on a large ontology with a great number of concepts, the proposed method achieves 62.35% of accuracy for a number of Web tables.