Recently, a significant number of studies have focused on knowledge graph completion using rule-enhanced learning techniques, supported by the mined soft rules in addition to the hard logic rules. However, due to the difficulty in determining the confidence of the soft rules without the global semantics of knowledge graph such as the semantic relatedness between relations, the knowledge representation may not be optimal, leading to degraded effectiveness in its application to knowledge graph completion tasks. To address this challenge, this paper proposes a retrofit framework that iteratively enhances the knowledge representation and confidence of soft rules. Specifically, the soft rules guide the learning of knowledge representation, and the representation, in turn, provides global semantics of the knowledge graph to optimize the confidence of soft rules. Extensive evaluation shows that our method achieves state-of-the-art results on link prediction and triple classification tasks, brought by the fine-tuned soft rules.

近来，大量研究集中在使用规则增强的学习技术来完成知识图的研究上，除了硬逻辑规则外，还由挖掘的软规则支持。但是，由于在没有知识图的全局语义（例如关系之间的语义相关性）的情况下难以确定软规则的置信度，因此知识表示可能不是最佳的，从而导致其在应用于知识图完成任务中的有效性降低。为了应对这一挑战，本文提出了一种改进框架，以迭代方式增强软规则的知识表示和可信度。具体来说，软规则指导知识表示的学习，而表示又反过来，提供知识图的全局语义，以优化软规则的置信度。广泛的评估表明，我们的方法在微调的软规则带来的链接预测和三重分类任务方面取得了最新的成果。