Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion

TL;DR

This paper introduces neuro-symbolic AI methods for knowledge base completion, combining rule-based models with graph embeddings to improve accuracy and interpretability in relation path analysis.

Contribution

It proposes two novel neuro-symbolic approaches for rule-based KBC and demonstrates their superiority over existing methods by integrating relation and path mixtures.

Findings

Outperforms state-of-the-art rule-based KBC by 2-10% in mean reciprocal rank

Combining rule-based models with graph embeddings further improves accuracy

Addresses non-uniformity of relation paths in knowledge bases

Abstract

Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particular, rule-based KBC has led to interpretable rules while being comparable in performance with graph embeddings. Even within rule-based KBC, there exist different approaches that lead to rules of varying quality and previous work has not always been precise in highlighting these differences. Another issue that plagues most rule-based KBC is the non-uniformity of relation paths: some relation sequences occur in very few paths while others appear very frequently. In this paper, we show that not all rule-based KBC models are the same and propose two distinct approaches that learn in one case: 1) a mixture of relations and the other 2) a mixture of paths. When implemented on top of neuro-symbolic AI, which learns rules by extending Boolean logic to real-valued logic, the latter model leads to superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in terms of mean reciprocal rank. Furthermore, to address the non-uniformity of relation paths, we combine rule-based KBC with graph embeddings thus improving our results even further and achieving the best of both worlds.