Standing on the Shoulders of Predecessors: Meta-Knowledge Transfer for Knowledge Graphs

• URL: http://arxiv.org/abs/2110.14170v1
• Date: Wed, 27 Oct 2021 04:57:16 GMT
• Title: Standing on the Shoulders of Predecessors: Meta-Knowledge Transfer for Knowledge Graphs
• Authors: Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen
• Abstract summary: We call such knowledge meta-knowledge, and refer to the problem of transferring meta-knowledge from constructed (source) KGs to new (target) KGs. MorsE represents the meta-knowledge via Knowledge Graph Embedding and learns the meta-knowledge by Meta-Learning. MorsE is able to learn and transfer meta-knowledge between KGs effectively, and outperforms existing state-of-the-art models.
• Score: 8.815143812846392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) have become widespread, and various knowledge graphs are constructed incessantly to support many in-KG and out-of-KG applications. During the construction of KGs, although new KGs may contain new entities with respect to constructed KGs, some entity-independent knowledge can be transferred from constructed KGs to new KGs. We call such knowledge meta-knowledge, and refer to the problem of transferring meta-knowledge from constructed (source) KGs to new (target) KGs to improve the performance of tasks on target KGs as meta-knowledge transfer for knowledge graphs. However, there is no available general framework that can tackle meta-knowledge transfer for both in-KG and out-of-KG tasks uniformly. Therefore, in this paper, we propose a framework, MorsE, which means conducting Meta-Learning for Meta-Knowledge Transfer via Knowledge Graph Embedding. MorsE represents the meta-knowledge via Knowledge Graph Embedding and learns the meta-knowledge by Meta-Learning. Specifically, MorsE uses an entity initializer and a Graph Neural Network (GNN) modulator to entity-independently obtain entity embeddings given a KG and is trained following the meta-learning setting to gain the ability of effectively obtaining embeddings. Experimental results on meta-knowledge transfer for both in-KG and out-of-KG tasks show that MorsE is able to learn and transfer meta-knowledge between KGs effectively, and outperforms existing state-of-the-art models.

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