@article{oai:uec.repo.nii.ac.jp:00009102,
 author = {川村, 隆浩 and Kawamura, Takahiro and 長野, 伸一 and Nagano, Shinichi and 大須賀, 昭彦 and Ohsuga, Akihiko},
 issue = {2},
 journal = {人工知能学会論文誌, Transactions of the Japanese Society for Artificial Intelligence},
 month = {Jan},
 note = {Linked Open Data (LOD) has a graph structure in which nodes are represented by URIs, and thus LOD sets are connected and searched through different domains. In fact, however, 5% of the values are literal (string without URI) even in DBpedia, which is a de facto hub of LOD. Therefore, this paper proposes a method of identifying and aggregating literal nodes in order to give a URI to literals that have the same meaning and to promote data linkage. Our method regards part of the LOD graph structure as a block image, and then extracts image features based on Scale-Invariant Feature Transform (SIFT), and performs ensemble learning, which is well known in the field of computer vision. In an experiment, we created about 30,000 literal pairs from a Japanese music category of DBpedia Japanese and Freebase, and confirmed thatthe proposed method correctly determines literal identity with F-measure of 76--85%.},
 pages = {440--448},
 title = {Linked Data統合に向けたLiteral値マッチング手法の提案},
 volume = {30},
 year = {2015},
 yomi = {カワムラ, タカヒロ and ナガノ, シンイチ and オオスガ, アキヒコ}
}