Title | ||
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Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion |
Abstract | ||
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ABSTRACTAiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to enhance its semantic representation. However, noise neighbor information might be amplified when the neighborhood is excessively sparse and no neighbor is available to represent the few-shot relation. Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances. Thus, inferring complex relations in the few-shot scenario is difficult for FKGC models due to limited training instances. In this paper, we propose a few-shot relational learning with global-local framework to address the above issues. At the global stage, a novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods. For the local stage, a meta-learning based TransH (MTransH) method is designed to model complex relations and train our model in a few-shot learning fashion. Extensive experiments show that our model outperforms the state-of-the-art FKGC approaches on the frequently-used benchmark datasets NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on Wiki-One by the metric [email protected] |
Year | DOI | Venue |
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2021 | 10.1145/3404835.3462925 | Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Few-Shot Relation, Knowledge Graph Completion, Neighbor Information, Gating Mechanism, Meta-Learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 10 |
Name | Order | Citations | PageRank |
---|---|---|---|
Guanglin Niu | 1 | 1 | 2.05 |
Yang Li | 2 | 659 | 125.00 |
Chengguang Tang | 3 | 0 | 0.68 |
Ruiying Geng | 4 | 0 | 2.70 |
Jian Dai | 5 | 7 | 3.86 |
Qiao Liu | 6 | 0 | 0.34 |
Hao Wang | 7 | 216 | 56.92 |
Jian Sun | 8 | 0 | 2.70 |
Fei Huang | 9 | 2 | 7.54 |
Luo Si | 10 | 2498 | 169.52 |