Journal of Data and Information Science ›› 2022, Vol. 7 ›› Issue (2): 84-106.doi: 10.2478/jdis-2022-0009

• Research Papers • Previous Articles    

Learning Context-based Embeddings for Knowledge Graph Completion

Fei Pu(), Zhongwei Zhang, Yan Feng, Bailin Yang   

  1. School of Computer and Information Engineering Zhejiang Gongshang University, Hangzhou 310018, China
  • Received:2021-11-03 Revised:2022-01-13 Accepted:2022-03-10 Online:2022-05-20 Published:2022-04-19
  • Contact: Fei Pu E-mail:puf2008@gmail.com

Abstract:

Purpose: Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one embedding vector. This study aims to propose a polysemous embedding approach, named KG embedding under relational contexts (ContE for short), for missing link prediction.

Design/methodology/approach: ContE models and infers different relationship patterns by considering the context of the relationship, which is implicit in the local neighborhood of the relationship. The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors, which represent the contextual information of the relationship. Then, according to the position of the entity, the entity's polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.

Findings: ContE is a fully expressive, that is, given any ground truth over the triples, there are embedding assignments to entities and relations that can precisely separate the true triples from false ones. ContE is capable of modeling four connectivity patterns such as symmetry, antisymmetry, inversion and composition.

Research limitations: ContE needs to do a grid search to find best parameters to get best performance in practice, which is a time-consuming task. Sometimes, it requires longer entity vectors to get better performance than some other models.

Practical implications: ContE is a bilinear model, which is a quite simple model that could be applied to large-scale KGs. By considering contexts of relations, ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning, it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.

Originality/value: ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to. It decomposes a relation vector into two vectors, namely, forward impact vector and backward impact vector in order to capture the relational contexts. ContE has the same low computational complexity as TransE. Therefore, it provides a new approach for contextualized knowledge graph embedding.

Key words: Full expressiveness, Relational contexts, Knowledge graph embedding, Relation patterns, Link prediction