Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Keywords:Knowledge Graph, Link Prediction, Few-Shot Learning, Transformer
AbstractFew-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.
How to Cite
Liang, Y., Zhao, S., Cheng, B., Yin, Y., & Yang, H. (2022). Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13003-13004. https://doi.org/10.1609/aaai.v36i11.21638
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