Linked Data統合に向けたLiteral値マッチング手法の提案

• 川村 隆浩

  (株)東芝 研究開発センター

• 長野 伸一

  (株)東芝 研究開発センター

• 大須賀 昭彦

  電気通信大学 大学院情報システム学研究科

書誌事項

タイトル別名

• Proposal of Literal Matching Method toward Linked Data Integration

抄録

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%.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 30 (2), 440-448, 2015

  一般社団法人 人工知能学会

キーワード

• linked open data
• literal matching
• instance matching

詳細情報

• CRID: 1390001205109067520
• NII論文ID: 130004927389
• DOI: 10.1527/tjsai.30.440
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://uec.repo.nii.ac.jp/records/9102
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
  • DBpedia
  • KAKEN
• 抄録ライセンスフラグ: 使用不可