M y M y at S em E val-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection (2025)

Abstract: The Food Hazard Detection (SemEval-2025 Task 9) advances explainable classification of food-incident reports collected from web sources, including social media and regulatory agency websites, to support timely risk mitigation for public health and the economy. This task is complicated by a highly imbalanced, long-tail label distribution and the need for transparent, reliable AI. We present a robust Knowledge-Augmented Data approach that integrates Retrieval-Augmented Generation (RAG) with domain-specific knowledge from the PubMed API to enrich and balance the training data. Our method leverages domain-specific knowledge to expand datasets and curate high-quality data that enhances overall data integrity. We hypothesize that Knowledge-Augmented Data improves Macro-F1 scores, the primary evaluation metric. Our approach achieved a top-2 ranking across both subtasks, demonstrating its effectiveness in advancing NLP applications for food safety and contributing to more reliable food hazard detection.

## introduction the increasing volume of food incident reports from various online sources highlights an urgent need for automated detection systems. these reports come from social media and official food agency websites and reflect economic and public health risks associated with foodborne illnesses and contamination. semeval-2025 task 9 addresses these challenges by developing systems that classify food incident reports and predict potential hazards. current methodologies, particularly data augmentation from large language models (llms), face hurdles such as hallucination, which complicates the development of reliable, scalable solutions. these challenges are further exacerbated 1 https://github.com/phanben110/kad-foodhazard ## system overview our proposed method, knowledge-augmented data for food hazard detection, enhances the quality and diversity of training data by integrating external knowledge and advanced filtering techniques. as illustrated in figure 2 , it leverages llms combined with retrieval-augmented generation (rag) using external sources such as the pubmed api foot_0 to generate high-quality augmented data, boosting model robustness and performance. the framework consists of four main steps: (1) simplifying complex queries using llms to retrieve relevant external knowledge; (2) generating augmented samples based on the retrieved context; ## conclusion our research demonstrates that a knowledge-augmented data approach significantly improves the accuracy and reliability of food hazard detection. by integrating rag with advanced data filtering, our system achieved top rankings in semeval-2025 task 9, showcasing the effectiveness of domain-specific knowledge in addressing data limitations. this highlights the potential of knowledge-driven ai to enhance food safety by rapidly and accurately identifying incidents from diverse online sources. future work will optimize model efficiency for large-scale deployment and improve recall in product classification to maximize real-world impact. importantly, our results underscore the value of explainability and transparency, which are essential for building trust and facilitating adoption in practical food safety applications.