Knowledge reasoning with multiple relational paths

Abstract

Knowledge reasoning technology can infer new knowledge based on the existing entity relationship information in the knowledge graph to complement the knowledge graph. In the existing knowledge reasoning methods based on multi-step relationship paths, the contributions of entities and relationships in the relationship paths are not distinguished, nor are multiple relationship paths comprehensively considered. To solve these problems, we propose a knowledge reasoning model based on Bi-directional Short-Term Memory and Attention Mechanism. Firstly, the neighborhood, entity semantic category and relationship path information are integrated in the knowledge graph, and the Neighborhood Semantic Path Network model (NSPN) is constructed to obtain a hybrid representation of the multi-step relationship path. Secondly, the loss function and training process of NSPN are introduced. Finally, comparative experiments are carried out on the public dataset, and the results show that our model is superior to other models in relation prediction tasks and link prediction tasks, improves the computational efficiency and data sparsity, and provides a new idea for knowledge reasoning methods.

KEYWORDS:

• Knowledge graph
• knowledge reasoning
• relational path
• attention mechanism

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this work is included within this article.