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FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic
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AI
DOI：10.1007/978-3-032-09527-5_11.png)
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Abstract
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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:
Knowledge Graphs
Entity Alignment
Holistic Matching
Symbolic Reasoning
Fuzzy logic
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30
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