1 MODELING A SUPPLY CHAIN REFERENCE ONTOLOGY BASED ON A TOP-LEVEL ONTOLOGY Farhad Ameri Associate Professor Engineering Informatics Lab Texas State University San Marcos, Texas, USA ameri@txstate.edu Boonserm Kulvatunyou Research Scientist Systems Integration Division National Institute of Standards and Technologies (NIST) Gaithersburg, Maryland, USA serm@nist.gov ABSTRACT Several supply-chain ontologies have been introduced in the past decade with the promise of enabling supply chain interoperability. However, the existing supply-chain ontologies have several gaps with respect to completeness, logical consistency, domain accuracy, and the development approach. In this work, we propose a new, supply-chain, reference ontology that is 1) based on an existing top-level ontology and 2) developed using a collaborative, ontology-development, best practice. We chose this approach because empirical studies have shown the usefulness of adopting a top-level ontology both for improving the efficiency of the development process and enhancing the quality of the resulting ontology. The proposed proof-of-concept reference ontology is developed in the context of the Industrial Ontology Foundry (IOF). IOF is an international effort aimed at providing a coherent set of modular and publicly-available ontologies for the manufacturing sector. Although the proposed reference ontology is still at the draft stage, this paper shows that it has already benefited from the collaborative development process that involves inputs from the other working groups within IOF. Additionally, as a way to validate the proposed reference ontology, an application ontology related to a supplier discovery and evaluation use case is derived from the reference ontology and tested. Keywords: supply chain, reference ontology, manufacturing, collaborative ontology development, interoperability INTRODUCTION Supply chains are increasingly more complex, digital, and dynamic. In this context, supply-chain integration is a necessary feature to enable enhanced coordination and communication among various supply chain participants such as vendors, service providers, and customers [1]. Many supply chain researchers and practitioners support the idea that supply chain efficiency can be improved with seamless flow of information [2]. One of the main enablers of such a seamless flow of information is interoperability. Interoperability is the ability of two or more systems to exchange information and interpret the exchanged information meaningfully and accurately in order to produce useful results via deference to a common information exchange reference model [3]. To date, supply chain interoperability is still a major, unsolved problem. The existing supply chain solutions have not been able to achieve full or agile information integration, because they do not interoperate [4]. Lack of interoperability can be attributed to differences in the underlying semantics and business rules implemented by different supply chain software systems. Ontologies have been proposed as the solution to these differences. Simply put, an ontology, which is a controlled vocabulary represented by formal logic, provides a consensus- based set of terms for describing the types of entities in a given domain and the relations between them [5]. In the supply chain domain, the core entities include the organizations that form the supply chain, their internal functions, capabilities, and resources, the buying and selling processes, the materials and the information that flow throughout the supply chains, and the processes and services that govern the operation and coordination of the supply chain. The first and most basic benefit of an ontology is that, like other kinds of standard data models such as entity- relationship model and XML Schema, it provides a common terminology that can be used for data annotation [6]. This common terminology enables both machines and humans to access, understand, search, and retrieve data more efficiently. A secondary benefit of ontology stems from its logic- based nature. Unlike other kinds of data models, logically formulated ontologies allow human and machine agents to make inferences about operations such as data aggregation, comparison, querying, and quality assurance. In addition, when data models are annotated or tagged by ontological entities, they become more easily searchable, combinable, and analyzable using logical-reasoning implemented by compatible software tools. These benefits are the main reasons that researchers have been proposing a growing number of supply chain ontologies [7-10]. Grubic and Fan [11] studied the existing supply-chain ontologies; they concluded that those ontologies have failed to