TY - GEN
T1 - An automatic ontology population with a machine learning technique from semi-structured documents
AU - Song, Hyun Je
AU - Park, Seong Bae
AU - Park, Se Young
PY - 2009
Y1 - 2009
N2 - 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
AB - 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
UR - https://www.scopus.com/pages/publications/70449646347
U2 - 10.1109/ICINFA.2009.5204981
DO - 10.1109/ICINFA.2009.5204981
M3 - Conference paper
AN - SCOPUS:70449646347
SN - 9781424436088
T3 - 2009 IEEE International Conference on Information and Automation, ICIA 2009
SP - 534
EP - 539
BT - 2009 IEEE International Conference on Information and Automation, ICIA 2009
T2 - 2009 IEEE International Conference on Information and Automation, ICIA 2009
Y2 - 22 June 2009 through 25 June 2009
ER -