@inproceedings{f8e0c986d36f4779b1fe7480922cf59b,
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.",
keywords = "Entity Alignment, Fuzzy logic, Holistic Matching, Knowledge Graphs, Symbolic Reasoning",
author = "Yiwen Peng and Thomas Bonald and Suchanek, \{Fabian M.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 24th International Semantic Web Conference, ISWC 2025 ; Conference date: 02-11-2025 Through 06-11-2025",
year = "2026",
month = jan,
day = "1",
doi = "10.1007/978-3-032-09527-5\_11",
language = "English",
isbn = "9783032095268",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "196--215",
editor = "Daniel Garijo and Sabrina Kirrane and Angelo Salatino and Cogan Shimizu and Maribel Acosta and Nuzzolese, \{Andrea Giovanni\} and Sebasti{\'a}n Ferrada and Thibaut Soulard and Kouji Kozaki and Hideaki Takeda and Gentile, \{Anna Lisa\}",
booktitle = "The Semantic Web – ISWC 2025 - 24th International Semantic Web Conference, 2025, Proceedings",
}