{"ID":2851137,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20467","arxiv_id":"2510.20467","title":"FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.20467","url_pdf":"https://arxiv.org/pdf/2510.20467v1","authors":"[\"Yiwen Peng\",\"Thomas Bonald\",\"Fabian M. Suchanek\"]","published":"2025-10-23T12:05:31Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.DB\"]","methods":"[\"LoRA\"]","has_code":false}