Traceability of food products to their sources is critical for quick responses to food emergencies. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that food traceability is a challenging task, including diversity of stakeholders and their lexicons, standards, tools, and methods; unwillingness to expose information about internal operations; lack of a common understanding of the steps in a supply chain; and incompleteness of data. The objective of this work is to address the traceability challenge by developing a formal ontology that can provide a shared and common understanding of the traceability model across all stakeholders in bulk food supply chains. A formal ontology can support semantic mediation, data integration, and data exploration, thus improving the intelligence and reliability of trace and track process. The Industrial Ontologies Foundry (IOF) procedures and principles are employed in the development of the supply chain traceability ontology. Basic Formal Ontology (BFO) is selected as the top-level ontology. A bottom-up approach is also adopted in a sense that a real use case related to the bulk grain domain is selected to be used for requirements definition and ontology validation. A software tool for visualization of the traceability graph is developed to validate the developed ontology based on simulated data. The test and validation results indicate that the developed ontology has the expressivity needed to represent the semantics of traceability data models within the scope of the selected use case. Also, it was observed that the developed supply chain traceability tool can effectively facilitate the track and trace process through visualizing the Resource Description Framework (RDF) triples, thus eliminating the need to formulate complex SPARQL queries for information retrievals.