Knowledge Commons of Institute of Automation,CAS
SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities | |
Li, Mingda1,2; Sun, Zhengya1,2; Zhang, Wensheng1,2 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2022-06-28 | |
卷号 | 491页码:186-196 |
摘要 | 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. |
关键词 | Knowledge graph embedding Out-of-knowledge-graph entities Knowledge graph completion Similarity search |
DOI | 10.1016/j.neucom.2022.03.063 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National 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] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000788143600016 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 知识表示与推理 |
国重实验室规划方向分类 | 可解释人工智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48391 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Sun, Zhengya |
作者单位 | 1.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 |
第一作者单位 | 精密感知与控制研究中心 |
通讯作者单位 | 精密感知与控制研究中心 |
推荐引用方式 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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