FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic

2025-10-23

AI Summary

Knowledge graph alignment aims to identify equivalent entities and relations across heterogeneous knowledge graphs; however, existing approaches predominantly rely on supervised, entity-level matching, suffering from limited interpretability and poor generalization. This paper proposes the first unsupervised, fuzzy logic–based framework for joint knowledge graph alignment, simultaneously modeling equivalence at both entity and relation levels while accommodating dangling entities. Leveraging fuzzy inference, embedding similarity guidance, and iterative optimization, our method achieves globally consistent, theoretically convergent, and interpretable alignment without requiring any labeled data. Experimental results demonstrate that it significantly outperforms state-of-the-art unsupervised methods across multiple mainstream benchmarks. Moreover, it provides transparent, step-by-step reasoning paths and explicit alignment justifications, enhancing both trustworthiness and analytical utility.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Unsupervised alignment of knowledge graphs without training data
• Holistic entity and relation matching using fuzzy logic
• Interpretable reasoning for cross-graph entity equivalence

Innovation

• Unsupervised alignment without requiring training data
• Holistic entity-relation alignment using fuzzy logic
• Interpretable reasoning with provable convergence guarantees