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SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities
Li, Mingda1,2; Sun, Zhengya1,2; Zhang, Wensheng1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2022-06-28
Volume491Pages:186-196
Abstract

Knowledge graph embedding acts as a pivotal role in predicting the missing information in knowledge graphs (KGs). Due to the evolving nature of real-world KGs, one requires the ability to make predictions for newly emerging entities besides those already observed at training time. Current studies have made great efforts to develop a neighborhood aggregator and embed out-of-knowledge-graph (OOKG) entities inductively, with less focus on exploiting the similarity between the existing and newly emerging enti-ties. Attaching importance to such similarity helps facilitate semantic transfer. In this work, we propose a similarity-aware aggregation network for embedding out-of-knowledge-graph entities. Motivated by the fact that similar entities are likely to occur in common graph context, we skillfully design a similarity-aware function, which measures the distance of each entity pair based on the contextual gap. Moreover, we aggregate the neighborhood surrounding the target entity and its similarity information by query-specific attention weights, which are optimized during the learning process. Extensive experi-ments on knowledge graph completion task show that our method achieves substantial improvements over baselines.(c) 2022 Elsevier B.V. All rights reserved.

KeywordKnowledge graph embedding Out-of-knowledge-graph entities Knowledge graph completion Similarity search
DOI10.1016/j.neucom.2022.03.063
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018AAA0102100] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000788143600016
PublisherELSEVIER
Sub direction classification知识表示与推理
planning direction of the national heavy laboratory可解释人工智能
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48391
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorSun, Zhengya
Affiliation1.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Mingda,Sun, Zhengya,Zhang, Wensheng. SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities[J]. NEUROCOMPUTING,2022,491:186-196.
APA Li, Mingda,Sun, Zhengya,&Zhang, Wensheng.(2022).SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities.NEUROCOMPUTING,491,186-196.
MLA Li, Mingda,et al."SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities".NEUROCOMPUTING 491(2022):186-196.
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