SIGIR2021: Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion. Paper
This is our source code and data for the paper:
Guanglin Niu, Yang Li, Chengguang Tang, Ruiying Geng, Jian Dai, Qiao Liu, Hao Wang, Jian Sun, Fei Huang, Luo Si. Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion. SIGIR 2021.
Author: Dr. Guanglin Niu (beihangngl at buaa.edu.cn)
Aiming 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 Hits@10.
The dataset can be downloaded from Drive. Unzip it to the directory ./GANA-FewShotKGC.
The Structure of the project is as followings:
GANA-FewShotKGC
|--./NELL
|--trainer_gana.py
|--params.py
|--models_gana.py
|--main_gana.py
|--hyper_embedding.py
|--embedding.py
|--data_loader.py
CUDA_VISIBLE_DEVICES=0 python main_gana.py --dataset NELL-One --data_path ./NELL \
--few 5 --data_form Pre-Train \
--prefix nellone_gana5 --max_neighbor 100
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