Abstract:

In order to enhance the prediction accuracy and interpretability of knowledge graph representation, by improving the IterE framework consisting of three modules：representation learning, rule learning and rule fusion, a knowledge graph representation learning method jointing FOL rules is proposed for many representation learning algorithms, aiming at the rule learning and fusion module, rules confidence calculation method is improved based on the triples scoring function to expand applicability, and soft labels calculation method is improved to relax fusion requirements and expand fused data increment, to iteratively implement representation update rules and rules enhanced representation. Link prediction and generating explanations experiments show that adding logic rules makes base model improve the prediction accuracy and interpretability, and it helps to improve the representation of sparse entities in more sparse data sets.

Key words: knowledge graph completion, representation learning, FOL rules, interpretability, sparse entities

摘要：

为增强知识图谱表示的预测精度和可解释性，通过改进由表示学习、规则学习和规则融合三个模块组成的IterE框架，提出一种适用各种表示学习算法的联合FOL规则的知识图谱表示学习方法，针对规则学习和融合模块，基于三元组打分函数改进规则置信度计算方法，扩展适用性，并改进软标签计算方法，放松融合要求，扩大融合的数据增量，迭代实现表示更新规则和规则增强表示。链路预测和生成解释实验表明，随着逻辑规则的加入，该方法提高了基模型的预测精度和可解释性，且在越稀疏的数据集中对提高稀疏实体表示的帮助越大。

关键词: 知识图谱补全, 表示学习, FOL规则, 可解释, 稀疏实体