Neural Probabilistic Logic Learning for Knowledge Graph Reasoning

TL;DR

This paper introduces Neural Probabilistic Logic Learning (NPLL), a framework that combines embedding networks with probabilistic logic to improve reasoning accuracy and interpretability on large-scale knowledge graphs.

Contribution

The paper proposes NPLL, integrating a scoring module and Markov Logic Networks with variational inference to enhance reasoning accuracy and interpretability.

Findings

Significantly improves reasoning accuracy on benchmark datasets

Balances model simplicity with reasoning capabilities

Enhances interpretability through probabilistic logic integration

Abstract

Knowledge graph (KG) reasoning is a task that aims to predict unknown facts based on known factual samples. Reasoning methods can be divided into two categories: rule-based methods and KG-embedding based methods. The former possesses precise reasoning capabilities but finds it challenging to reason efficiently over large-scale knowledge graphs. While gaining the ability to reason over large-scale knowledge graphs, the latter sacrifices reasoning accuracy. This paper aims to design a reasoning framework called Neural Probabilistic Logic Learning(NPLL) that achieves accurate reasoning on knowledge graphs. Our approach introduces a scoring module that effectively enhances the expressive power of embedding networks, striking a balance between model simplicity and reasoning capabilities. We improve the interpretability of the model by incorporating a Markov Logic Network based on variational inference. We empirically evaluate our approach on several benchmark datasets, and the experimental results validate that our method substantially enhances the accuracy and quality of the reasoning results.