Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. In this paper, we propose FLORA, a simple yet effective method that (1) is unsupervised, i.e., does not require training data, (2) provides a holistic alignment for entities and relations iteratively, (3) is based on fuzzy logic and thus delivers interpretable results, (4) provably converges, (5) allows dangling entities, i.e., entities without a counterpart in the other KG, and (6) achieves state-of-the-art results on major benchmarks.

翻译：知识图谱对齐任务旨在匹配两个知识图谱中等价的实体（即实例与类）及关系。现有方法大多聚焦于纯实体层面的对齐，通过在某种嵌入空间中计算实体相似度实现。这些方法缺乏可解释的推理过程，且需要训练数据才能运行。本文提出FLORA方法，该方案简洁而高效，具有以下特性：(1) 无监督性，即无需训练数据；(2) 通过迭代方式实现实体与关系的整体对齐；(3) 基于模糊逻辑框架，可提供可解释的结果；(4) 具备可证明的收敛性；(5) 支持悬空实体（即在另一知识图谱中无对应项的实体）的处理；(6) 在主流基准测试中达到最优性能。