Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG

• URL: http://arxiv.org/abs/2603.09758v1
• Date: Tue, 10 Mar 2026 14:57:34 GMT
• Title: Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
• Abstract summary: This paper presents a modelagnostic pipeline that retrieves NEL entities from domain-OnRAG.<n>The pipeline approaches state-of-the-art accuracy revealing gaps and inconsistencies in existing annotations.<n>The design avoids fine-tuning, improves robustness to evolution, and yields interpretable decisions through grounded justifications.
• Score: 3.6073664126704537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standardizing food terms from product labels and menus into ontology concepts is a prerequisite for trustworthy dietary assessment and safety reporting. The dominant approach to Named Entity Linking (NEL) in the food and nutrition domains fine-tunes Large Language Models (LLMs) on task-specific corpora. Although effective, fine-tuning incurs substantial computational cost, ties models to a particular ontology snapshot (i.e., version), and degrades under ontology drift. This paper presents FoodOntoRAG, a model- and ontology-agnostic pipeline that performs few-shot NEL by retrieving candidate entities from domain ontologies and conditioning an LLM on structured evidence (food labels, synonyms, definitions, and relations). A hybrid lexical--semantic retriever enumerates candidates; a selector agent chooses a best match with rationale; a separate scorer agent calibrates confidence; and, when confidence falls below a threshold, a synonym generator agent proposes reformulations to re-enter the loop. The pipeline approaches state-of-the-art accuracy while revealing gaps and inconsistencies in existing annotations. The design avoids fine-tuning, improves robustness to ontology evolution, and yields interpretable decisions through grounded justifications.

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