Abstract
With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.
Highlights
With the development of science and technology, significant progress has been achieved in robotics that the types and application fields of robots are constantly enriched
We mainly introduce the work related to our Large-scale Knowledge Graph reasoning and completion methods for robots
This section begins by introducing some notations and definitions used in the rest of this article. This is followed by an introduction of our encoder model GI-KBGAT, an improved Graph Attention Network for Knowledge Graphs (KG), which considers gate mechanism on multi-head attention and interaction between entities and relations to generate embeddings
Summary
Reviewed by: Xiang Lin, Shanghai Jiao Tong University, China Li Xiaoyong, Beijing University of Posts and Telecommunications (BUPT), China. With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. Studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Most existing methods for knowledge graph completion focus on entity representation learning. The importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. We propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237
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