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arXiv · 2510.20467

FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic

Yiwen Peng·Thomas Bonald·Fabian M. Suchanek
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Abstract

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.

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Yiwen Peng, Thomas Bonald, Fabian M. Suchanek. 2025-10-23. FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic. https://arxiv.org/abs/2510.20467

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