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dc.contributor.authorPei, Shichao
dc.contributor.authorYu, Lu
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2019-08-19T12:04:01Z
dc.date.available2019-08-19T12:04:01Z
dc.date.issued2019-05-13
dc.identifier.citationPei, S., Yu, L., Hoehndorf, R., & Zhang, X. (2019). Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference. The World Wide Web Conference on - WWW ’19. doi:10.1145/3308558.3313646
dc.identifier.doi10.1145/3308558.3313646
dc.identifier.urihttp://hdl.handle.net/10754/656520
dc.description.abstractEntity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions: 1) labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used; and 2) knowledge graph embedding is affected by entity's degree difference, which brings challenges to align high frequent and low frequent entities. We propose a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment. Furthermore, we improve the knowledge graph embedding with awareness of the degree difference by performing the adversarial training. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-19-01.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=3308558.3313646
dc.rightsArchived with thanks to WWW '19 The World Wide Web Conference
dc.subjectKnowledge Graph
dc.subjectEntity Alignment
dc.subjectSemi-supervised Learning
dc.titleSemi-supervised entity alignment via knowledge graph embedding with awareness of degree difference
dc.typeConference Paper
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.conference.date2019-05-13 to 2019-05-17
dc.conference.name2019 World Wide Web Conference, WWW 2019
dc.conference.locationSan Francisco, CA, USA
dc.eprint.versionPost-print
kaust.personPei, Shichao
kaust.personYu, Lu
kaust.personHoehndorf, Robert
kaust.personZhang, Xiangliang
kaust.grant.numberFCC/1/1976-19-01
refterms.dateFOA2019-08-22T11:55:43Z
dc.date.published-online2019-05-13
dc.date.published-print2019


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