文献类型：会议论文

英文题名：Effective Online Knowledge Graph Fusion

作者：Wang, Haofen[1];Fang, Zhijia[1];Zhang, Le[1];Pan, Jeff Z.[2];Ruan, Tong[1]

机构：[1]E China Univ Sci & Technol, Shanghai 200237, Peoples R China;[2]Univ Aberdeen, Aberdeen, Scotland

会议论文集：14th International Semantic Web Conference (ISWC)

会议日期：OCT 11-15, 2015

会议地点：Bethlehem, PA

语种：英文

外文关键词：Knowledge graph - Ontology - Search engines

摘要：Recently, Web search engines have empowered their search with knowledge graphs to satisfy increasing demands of complex information needs about entities. Each engine offers an online knowledge graph service to display highly relevant information about the query entity in form of a structured summary called knowledge card. The cards from different engines might be complementary. Therefore, it is necessary to fuse knowledge cards from these engines to get a comprehensive view. Such a problem can be considered as a new branch of ontology alignment, which is actually an on-the-fly online data fusion based on the users' needs. In this paper, we present the first effort to work on knowledge cards fusion. We propose a novel probabilistic scoring algorithm for card disambiguation to select the most likely entity a card should refer to. We then design a learning-based method to align properties from cards representing the same entity. Finally, we perform value deduplication to group equivalent values of the aligned properties as value clusters. The experimental results show that our approach outperforms the state of the art ontology alignment algorithms in terms of precision and recall.