Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
arXiv:2605.25814v1 Announce Type: new Abstract: Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in data management and mining. However, the dominant blocking-matching-clustering paradigm for ER suffers from critical flaws. Its cascaded, decoupled workflow essentially produces a static, sparse graph plagued by missing edges (due to blocking failures) and noisy links (due to matching errors), causing error propagation and yielding suboptimal clusters, particularly when rigid transitivity is imposed
The proliferation of messy and large datasets makes efficient and accurate entity resolution increasingly critical, driving innovation in AI-driven solutions.
Improved entity resolution directly enhances data quality and integration, which are foundational for advanced AI applications, analytics, and business intelligence.
The use of LLMs for adaptive graph refinement and label propagation offers a more robust and cost-effective approach to entity resolution than traditional methods.
- · Data-intensive industries
- · AI/ML platform providers
- · Data scientists
- · Knowledge graph developers
- · Traditional data cleaning services
- · Organizations with siloed, dirty data
Data integration processes become significantly more efficient and accurate, reducing operational costs.
Higher quality foundational data accelerates the development and deployment of more reliable AI agents and systems.
Enhanced data insights lead to improved decision-making across various sectors, potentially altering competitive landscapes.
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Read at arXiv cs.CL