Modeling Complex Relationship Paths for Knowledge Graph Completion

Qingping TAN
Xiankai MENG
Jianjun XU

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D    No.5    pp.1393-1400
Publication Date: 2018/05/01
Publicized: 2018/02/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7398
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
knowledge graph completion,  knowledge representation learning,  knowledge graph,  

Full Text: PDF>>
Buy this Article

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translation models obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task.