PPKE: Knowledge Representation Learning by Path-based Pre-training

• URL: http://arxiv.org/abs/2012.03573v1
• Date: Mon, 7 Dec 2020 10:29:30 GMT
• Title: PPKE: Knowledge Representation Learning by Path-based Pre-training
• Authors: Bin He, Di Zhou, Jing Xie, Jinghui Xiao, Xin Jiang, Qun Liu
• Abstract summary: We propose a Path-based Pre-training model to learn Knowledge Embeddings, called PPKE. Our model achieves state-of-the-art results on several benchmark datasets for link prediction and relation prediction tasks.
• Score: 43.41597219004598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entities may have complex interactions in a knowledge graph (KG), such as multi-step relationships, which can be viewed as graph contextual information of the entities. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, and neglect most of the graph contextual information exists in the topological structure of KGs. In this study, we propose a Path-based Pre-training model to learn Knowledge Embeddings, called PPKE, which aims to integrate more graph contextual information between entities into the KRL model. Experiments demonstrate that our model achieves state-of-the-art results on several benchmark datasets for link prediction and relation prediction tasks, indicating that our model provides a feasible way to take advantage of graph contextual information in KGs.

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