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■ページ数 11ページ

社会課題解決に向けたナレッジグラフと欠損推定手法の提案― 学内駐輪環境改善の試み ―


Proposal of Knowledge Graph and Completion Method for Solving Social Issues: An Attempt to Improve the Bicycle Parking Environment in the University

■著者名 塚越 雄登(電気通信大学大学院),川村 隆浩(農業・食品産業技術総合研究機構),清 雄一(電気通信大学大学院),田原 康之(電気通信大学大学院),大須賀 昭彦(電気通信大学大学院)
■著者名(英語) Yuto Tsukagoshi (Graduate School of Informatics and Engineering, The University of Electro-Communications), Takahiro Kawamura (National Agriculture and Food Research Organization), Yuichi Sei (Graduate School of Informatics and Engineering, The University of Electro-Communications), Yasuyuki Tahara (Graduate School of Informatics and Engineering, The University of Electro-Communications), Akihiko Ohsuga (Graduate School of Informatics and Engineering, The University of Electro-Communications)
■価格 会員 ¥550 一般 ¥770
■書籍種類 論文誌(論文単位)
■グループ名 【C】電子・情報・システム部門
■本誌 電気学会論文誌C(電子・情報・システム部門誌) Vol.140 No.8 (2020) 特集T:社会課題解決に向けた超スマート社会実現技術 特集U:国際会議ICESS 2019
■本誌掲載ページ 905-915ページ
■原稿種別 論文/日本語
■電子版へのリンク https://www.jstage.jst.go.jp/article/ieejeiss/140/8/140_905/_article/-char/ja/
■キーワード ナレッジグラフ,Linked open data,欠損推定,Translation-based model  knowledge graph,Linked open data,knowledge graph completion,Translation-based model
■要約(英語) Societies in Japan face many social issues today. To address these issues, the government is exploring ways to solve problems using data. The purpose of this study is to provide support using existing data for grasping the current situation, taking up the problem of bicycle parking environment in a university as an example of social issues. Specifically, we tried to design a unique schema and build knowledge graph for integration of various on-campus data. Then we applied and modified the knowledge graph completion methods to improve efficiency and accuracy. For the knowledge graph constructed in this study, the number of bicycles was estimated by the conventional method and the proposed method, and the transition of the sum of absolute value errors was compared. The proposed method exceeded the existing method in efficiency and accuracy. In addition, approximately 650 data aggregated at any date and place were able to be estimated about 54 units correctly, compared to the conventional method. Finally, we improved efficiency and accuracy of knowledge graph completion methods.
■版 型 A4
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