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.

中文翻译：

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通过神经符号 AI 结合规则和嵌入来完成知识库

最近对知识库完成 (KBC) 的兴趣导致了大量基于强化学习、归纳逻辑编程和图嵌入的方法。特别是，基于规则的 KBC 导致了可解释的规则，同时在性能上可与图嵌入相媲美。即使在基于规则的 KBC 中，也存在导致不同质量规则的不同方法，而且以前的工作在突出这些差异方面并不总是准确的。困扰大多数基于规则的 KBC 的另一个问题是关系路径的不均匀性：一些关系序列出现在很少的路径中，而其他关系序列出现得非常频繁。在本文中，我们表明并非所有基于规则的 KBC 模型都是相同的，并提出了两种不同的方法，在一种情况下学习：1）关系的混合和另一种 2）路径的混合。当在神经符号 AI 之上实施时，通过将布尔逻辑扩展到实值逻辑来学习规则，后一种模型导致卓越的 KBC 准确度比最先进的基于规则的 KBC 高出 2-10%平均倒数排名。此外，为了解决关系路径的不一致性，我们将基于规则的 KBC 与图嵌入相结合，从而进一步改善我们的结果并实现两全其美。