Abstract
Entity linking (EL) involves identifying references to entities in source data, which can be structured or unstructured, and associating them with their respective records in a structured knowledge base. This technique is beneficial when applied to tabular data, facilitating tasks such as data integration, business intelligence, and the construction of knowledge graphs. Current EL algorithms for tabular data often face challenges such as ambiguity, heterogeneity, and limited context. In this paper, we propose TabLinkLLM - a generic approach for tabular data entity linking using Large Language Models (LLMs), specifically optimized through prompt engineering, retrieval-augmented generation, and fine-tuning. Our experimental comparisons with leading EL models reveal that while our approach does not outperform specialized EL models in terms of performance, the broad knowledge base of LLMs proves advantageous in addressing scenarios that these specialized models cannot handle.