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📄 Abstract
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.
Authors (3)
Yiwen Peng
IP Paris
Thomas Bonald
IP Paris
Fabian M. Suchanek
IP Paris
Submitted
October 23, 2025
The 24th International Semantic Web Conference (ISWC), Nov 2025,
Nara / Japan, Japan
Key Contributions
Proposes FLORA, an unsupervised method for knowledge graph alignment based on fuzzy logic. FLORA provides a holistic, interpretable alignment for entities and relations, guarantees convergence, handles dangling entities, and achieves state-of-the-art results without requiring training data.
Business Value
Facilitates the integration of disparate knowledge sources, enabling richer data analysis, improved search capabilities, and more robust AI systems by creating unified knowledge representations.