FARM: A Multi-Agent Framework for Automated Construction of Multi-Species Livestock Health Knowledge Graphs
Songxue Zhang, Shanshan Cao, Nan Ma, Wei Sun, Fantao Kong · Agriculture · 2026
Livestock health knowledge graphs are essential for decision-making and reasoning in animal husbandry, yet existing knowledge is scattered across unstructured literature and encoded in narrowly scoped, species-specific models, resulting in semantic fragmentation and limited reusability. To address these issues, we proposed FARM (Four-dimensional Automated-Reasoning Multi-agent), a zero-shot multi-agent framework used for constructing multi-species livestock health knowledge graphs. FARM is grounded in a Four-Dimension Livestock Health Framework encompassing Rearing Environment, Physiological Status, Feed & Water Inputs, and Production Performance, and employs a unified ontology strategy that integrates cross-species general labels with species-specific constraints to achieve semantic alignment. The framework orchestrates five specialized agents—Coordination, Entity Extraction, Ontology Normalization, Relation Extraction, and Knowledge Fusion—to automate the construction process. Experiments on 2478 expertly annotated text samples demonstrate that FARM achieves an entity-level F1 score of 0.8070 (IoU ≥ 0.5), surpassing the strongest baseline by 0.1627. Moreover, it attains a corrected entity label accuracy of 90.44% and an F1 score of 0.9277 in relation existence identification, outperforming the baseline by 0.1114. Validation on 500 image samples further confirms its capability in multimodal evidence fusion. The resulting knowledge graph contains 29,064 entities and 26,662 triples, providing a reusable foundation for zero-shot extraction and unified cross-species semantic modeling.