Year: 2017
Type: article
Abstract: Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper, we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs. Previous models such as Trans (E, H, R) and CTransR are either insufficient for embedding hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be effective for generalization nor accurately reflect real knowledge. To overcome these issues, we propose the novel model TransHR, which transforms the hyper-relations in a pair of entities into an individual vector, serving as a translation between them. We experimentally evaluate our model on two typical tasks-link prediction and triple classification. The results demonstrate that TransHR significantly outperforms Trans (E, H, R) and CTransR, especially for hyperrelational data.
Source: Tsinghua Science & Technology
Authors: Chunhong Zhang, Miao Zhou, Xiao Han, Zheng Hu, Yang Ji
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2608223753