Show simple item record

dc.contributor.authorFu, Chenpeng
dc.contributor.authorLi, Zhixu
dc.contributor.authorYang, Qiang
dc.contributor.authorChen, Zhigang
dc.contributor.authorFang, Junhua
dc.contributor.authorZhao, Pengpeng
dc.contributor.authorXu, Jiajie
dc.date.accessioned2020-03-03T05:51:30Z
dc.date.available2020-03-03T05:51:30Z
dc.date.issued2019-11-14
dc.identifier.citationFu, C., Li, Z., Yang, Q., Chen, Z., Fang, J., Zhao, P., & Xu, J. (2019). Multiple Interaction Attention Model for Open-World Knowledge Graph Completion. Lecture Notes in Computer Science, 630–644. doi:10.1007/978-3-030-34223-4_40
dc.identifier.doi10.1007/978-3-030-34223-4_40
dc.identifier.urihttp://hdl.handle.net/10754/661852
dc.description.abstractKnowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity’s name and parts of its text-description to connect unseen entities to the KG. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. Our empirical study conducted on two real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/978-3-030-34223-4_40
dc.rightsArchived with thanks to Springer International Publishing
dc.titleMultiple Interaction Attention Model for Open-World Knowledge Graph Completion
dc.typeConference Paper
dc.contributor.departmentKing Abdullah University of Science and Technology, Jeddah, Saudi Arabia
dc.rights.embargodate2020-11-14
dc.conference.date2019-11-26 to 2019-11-30
dc.conference.name20th International Conference on Web Information Systems Engineering, WISE 2019
dc.conference.locationHongkong, HKG
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, China
dc.contributor.institutioniFLYTEK Research, Suzhou, China
dc.contributor.institutionState Key Laboratory of Cognitive Intelligence, iFLYTEK, Hefei, People’s Republic of China
kaust.personYang, Qiang


This item appears in the following Collection(s)

Show simple item record