Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

• URL: http://arxiv.org/abs/2211.08169v1
• Date: Tue, 15 Nov 2022 14:23:07 GMT
• Title: Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
• Authors: Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp
• Abstract summary: We propose a few-shot out-of-graph (OOG) link prediction task for temporal knowledge graphs (TKGs) We predict the missing entities from the links concerning unseen entities by employing a meta-learning framework. Our model achieves superior performance on all three datasets.
• Score: 31.10140298420744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities. Compared to KGs, TKGs require temporal reasoning techniques for modeling, which naturally increases the difficulty in dealing with novel, yet unseen entities. In this work, we focus on the inductive learning of unseen entities' representations on TKGs. We propose a few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the missing entities from the links concerning unseen entities by employing a meta-learning framework and utilizing the meta-information provided by only few edges associated with each unseen entity. We construct three new datasets for TKG few-shot OOG link prediction, and we propose a model that mines the concept-aware information among entities. Experimental results show that our model achieves superior performance on all three datasets and our concept-aware modeling component demonstrates a strong effect.

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