FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914109138665472 |
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| author | Peng, Yiwen Bonald, Thomas Suchanek, Fabian M. |
| author_facet | Peng, Yiwen Bonald, Thomas Suchanek, Fabian M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic Peng, Yiwen Bonald, Thomas Suchanek, Fabian M. Artificial Intelligence Databases 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. |
| title | FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic |
| topic | Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2510.20467 |