AICLMar 13, 2020

Knowledge Graph Alignment using String Edit Distance

arXiv:2003.12145v2
AI Analysis

This addresses the problem of aligning knowledge graphs for researchers and practitioners, but it appears incremental as it builds on existing string edit distance methods.

The paper tackles knowledge graph alignment by proposing a technique based on string edit distance that leverages entity type information and handles relations of any arity, but no concrete results or numbers are provided.

In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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