FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic

TL;DR

FLORA is an unsupervised, interpretable knowledge graph alignment method using fuzzy logic that iteratively aligns entities and relations, handles dangling entities, and achieves state-of-the-art performance.

Contribution

The paper introduces FLORA, a novel unsupervised and interpretable approach for holistic knowledge graph alignment based on fuzzy logic, with proven convergence and superior benchmark results.

Findings

Achieves state-of-the-art results on major benchmarks.

Provides interpretable alignment results.

Handles dangling entities effectively.

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.