FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic
This addresses the need for interpretable and data-efficient alignment in knowledge graphs, though it appears incremental as it builds on existing alignment tasks.
The paper tackles the problem of knowledge graph alignment by proposing FLORA, an unsupervised method that uses fuzzy logic to align entities and relations without training data, achieving state-of-the-art results on major benchmarks.
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.