Neural Probabilistic Logic Learning for Knowledge Graph Reasoning

Fengsong Sun, Jinyu Wang, Zhiqing Wei, Xianchao Zhang

Keywords: Knowledge graph reasoning, embedding, rule-based, variational inference

TL;DR: We introduce a novel knowledge graph reasoning approach that significantly outperforms existing methods across multiple benchmark metrics.

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. We strike a balance between model simplicity and reasoning capabilities 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.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

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Submission Number: 5382