TY - GEN
T1 - Effective online knowledge graph fusion
AU - Wang, Haofen
AU - Fang, Zhijia
AU - Zhang, Le
AU - Pan, Jeff Z.
AU - Ruan, Tong
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84952304889&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-25007-6_17
DO - 10.1007/978-3-319-25007-6_17
M3 - Published conference contribution
AN - SCOPUS:84952304889
SN - 9783319250069
VL - 9366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 286
EP - 302
BT - The Semantic Web – ISWC 2015 - 14th International Semantic Web Conference, Proceedings
PB - Springer-Verlag
T2 - 14th International Semantic Web Conference, ISWC 2015
Y2 - 11 October 2015 through 15 October 2015
ER -