无需训练数据，用模糊逻辑实现知识图谱实体与关系的可解释对齐。
FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic
- 基于模糊逻辑构建无监督对齐框架，支持实体与关系联合迭代匹配。
- 在多个主流基准上达到当前最优性能，且能处理无对应实体的情况。
- 结果可解释、理论收敛，适合缺乏标注数据的场景使用。
知识图谱对齐旨在匹配两个知识图谱中的等价实体（实例和类）与关系。现有方法多聚焦于纯实体级对齐，依赖嵌入空间中的相似性计算，缺乏可解释性且需训练数据。本文提出FLORA，一种简单而有效的无监督方法：(1) 不依赖训练数据；(2) 迭代式实现实体与关系的全局对齐；(3) 基于模糊逻辑，输出可解释结果；(4) 可证明收敛；(5) 支持孤立实体（即在另一图谱中无对应项）；(6) 在主要基准上取得当前最优表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。