Most of todays work on knowledge graph completion is concerned with
sub-symbolic approaches that focus on the concept of embedding a given graph in
a low dimensional vector space. Against this trend, we propose an approach
called AnyBURL that is rooted in the symbolic space. Its core algorithm is
based on sampling paths, which are generalized into Horn rules. Previously
published results show that the prediction quality of AnyBURL is on the same
level as current state of the art with the additional benefit of offering an
explanation for the predicted fact. In this paper, we are concerned with two
extensions of AnyBURL. Firstly, we change AnyBURLs interpretation of rules from
$\Theta$-subsumption into $\Theta$-subsumption under Object Identity. Secondly,
we introduce reinforcement learning to better guide the sampling process. We
found out that reinforcement learning helps finding more valuable rules earlier
in the search process. We measure the impact of both extensions and compare the
resulting approach with current state of the art approaches. Our results show
that AnyBURL outperforms most sub-symbolic methods.

本文提出了一种基于符号空间的知识图谱补全方法 AnyBURL，其核心算法基于路径抽样，并将其推广为 Horn 规则，利用强化学习指导抽样，实现更精确的预测并提供一个解释，实验结果表明 AnyBURL 优于大多数子符号方法。

知识图谱完成的增强型随时自下而上规则学习

Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph  Completion

Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt "pedagogical approaches" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.

本文探索了如何利用神经网络中的教育方法，从嵌入模型中提取加权 Horn 规则，以增强知识库的完整性和解释性。