Entity Linking in Invoices: Reducing Large Language Model Calls by Using Knowledge-Guided Heuristic Filtering - Azevedo, Pedro H.; Fileto, Renato; Zibetti, André W.; Werner, Simone S. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Accurately identifying invoice items is crucial for procurement auditing, but challenging due to noisy, non-standardized descriptions. Large Language Models (LLMs) alone solve this problem with limited performance, and entail high computational costs and privacy concerns. Thus, we propose a hybrid entity linking framework that combines index-based retrieval to match entity surface names in a knowledge base, knowledge-guided heuristic filtering of EL candidates, and an on-premise LLM to solve only persistent ambiguities. Evaluated on Brazilian medicine invoices, our filters reduced LLM inferences by 49.60% and tokens per call by 42.4%. Using qwen2.5:7b, our approach achieved 84.48% accuracy, outperforming a filterless baseline by over 21%.