Neural Theorem Provers Delineating Search Area Using RNN

TL;DR

This paper introduces RNNNTP, an improved neural theorem prover that enhances computational efficiency and interpretability for knowledge graph link prediction, achieving competitive results across multiple datasets.

Contribution

The paper proposes RNNNTP, a novel RNN-based neural theorem prover with an effective relation generator, improving efficiency and interpretability in link prediction tasks.

Findings

Achieves competitive link prediction performance on four datasets.

Significantly improves computational efficiency over traditional NTPs.

Demonstrates effective training of the relation generator.

Abstract

Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graphs link prediction has been limited due to their computational inefficiency. A new RNNNTP method is proposed in this paper, using a generalized EM-based approach to continuously improve the computational efficiency of Neural Theorem Provers(NTPs). The RNNNTP is divided into relation generator and predictor. The relation generator is trained effectively and interpretably, so that the whole model can be carried out according to the development of the training, and the computational efficiency is also greatly improved. In all four data-sets, this method shows competitive performance on the link prediction task relative to traditional methods as well as one of the current strong competitive methods.