Traceability of food products to their sources is critical for quick responses to food emergencies. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. Food trace-ability is challenging for a variety of reasons including diversity and heterogenicity of participants, complexity of the supply chain and its processes and lack of a common understanding of steps in a supply chain, and incompleteness of data, and unwillingness of actors to expose information of their internal operations. 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 a food supply chain. In previous research, an ontological approach was adopted to address the traceability problem in the context of a use case related to harvest to on-farm storage activities. This work extends the Supply Chain Traceability ontology by introducing additional critical tracking events including transform event, sampling event, observation event, custody change event, and ownership change event in the context of a scenario involving shipments of commodity grain from a primary grain elevator to a processor of grain such as a feed manufacturer. A knowledge graph is generated based on a simulated dataset and the ontology is validated through query, reasoning, and visualization conducted in the RDFox environment.

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.

This paper summarizes the results from recent activities of the IOF Supply Chain Working Group (SC WG). The objectives of the IOF SC WG are to identify requirements, notions, and terms from the supply chain domain, develop a domain-specific reference ontology (DSRO), and validate the developed ontology by widening the scope to multiple use cases across the domain. The development of the current reference ontology was motivated by exploring two use cases related to supplier discovery and supply chain traceability. A draft of the OWL ontology is available for download through the provided GitHub link. This paper is not intended to provide a detailed discussion on linguistic and axiomatic analysis of the ontological entities. Rather, the intention is to provide an overview of the objectives, accomplishment, and challenges of this working group and highlight the key discussion points for the IOF workshop of I-ESA 2020

The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, an effort called Industrial Ontologies Foundry (IOF) has been formed to pursue the same goal for the industrial manufacturing domain. However, developing a coherent ontology covering the entire industrial manufacturing domain has been known to be a mountainous challenge because of the multidisciplinary nature of manufacturing. To manage the scope and expectations, the IOF community kicked-off its effort with a proof-of-concept (POC) project. This paper describes the developments within the project. It also provides a brief update on the IOF organizational set up.

This document is a part of the definition of a reference architecture for the integration of manufacturing software applications in the areas of fabrication and assembly of discrete electro mechanical parts.This document captures, at a high level of abstraction, the engineering and operations functions involved in producing discrete products, and the general nature of information flows among them.The function elements provide a frame of reference for productand facility-specific functions in a production system, and thus for specifications for manufacturing software/hardware systems components.Assigning these narrower functions to components begets a systems architecture, for which the information flows described in this document identify the necessary interactions among the components.The information flow elements also provide a frame of reference for specifying the information involved in those interactions and in the corresponding component interfaces.In short, these models are part of a framework for specifying production systems' architectures, and the related architectures for the software systems that support production systems engineering.

Smart manufacturing system will be able to quickly adapt to new and changing requirements, implying that software and hardware components of the manufacturing system need to be easily recomposed. In addition, provision of software applications and components is trending toward distributed, heterogeneous, and cloud-based. However, engineers who need to compose a software system will have difficulty finding and using software components with the right functionality and compatibility without a standard to describe these software components. This paper identifies the need for a reference functional ontology to provide a common way to describe a software component functionality. Such an ontology would lead towards the needed standard to describe the software components. The objective of this paper is to discuss high-level requirements for such a functional ontology and provide an initial use case to illustrate how the functional ontology may enable composability analysis.

The National Institute for Standards and Technology sponsored a workshop in October, 2007, on the subject of ontology evaluation. An international group of invited experts met for two days to discuss problems in measuring ontology quality. The workshop highlighted several divisions among ontology developers regarding approaches to ontology evaluation. These divisions were generally reflective of the opinions of the participants. However, the workshop documented a paucity of empirical evidence in support of any particular position. Given the importance of ontologies to every knowledge-intensive human activity, there is an urgent need for research to develop an empirically derived knowledge base of best practices in ontology engineering and methods for assuring ontology quality over time. This is a report of the workshop discussion and brainstorming by the participants about what such a research program might look like.

In many data-centric applications it is desirable to use OWL as an expressive schema language where one expresses constraints that need to be satisfied by the (instance) data. However, some features of OWL’s semantics, specifically the Open World Assumption (OWL) and not having a Unique Name Assumption (UNA), make it hard to use OWL for this task. What would trigger a constraint violation in a closed world system like a relational database leads to new inferences in OWL. In this paper, we explore how OWL can be extended to accommodate integrity constraints and discuss several alternatives for the syntax and semantics of such an extension. We primarily focus on applications in the Supply Chain Management (SCM) domain but we are also gathering use cases and requirements from many other application areas to assess which of these alternatives provides the best solution.

This paper is an overview of the AMIS (Automated Methods for Integrating Systems) project approach to systems integration. The objective of the AMIS project is to reduce the cost and time for software integration by devising methods, algorithms, and tools by which activities of a systems engineer can be automated. The motivation for this work is to reduce the expense of integration efforts where traditional standards-based approaches are inappropriate or ineffective, e.g., where the time it takes to develop a standard is longer than the life of the integration problem. The anticipated benefits of this project include: improving interface/service specifications, improving knowledge capture for existing software systems and standards, reducing the time and cost of systems integration projects, identifying the unsolved problems, and providing knowledge for new toolkits. The AMIS approach is based on the idea that the published interface specifications for a software system can be abstracted into an understanding of the roles in the business processes the system was built to support. Those roles can be formalized into specifications and models for interactions in which each element will be associated with the corresponding business action/entity/property notion. When an engineer devises new processes, software may be able to match existing role definitions for component systems and implement those roles along with any needed choreography and process-specific wrappers that transform the physical message sequences on the basis of equivalent business notions.

: The building of knowledge-intensive real-time intelligent control systems is one of the most difficult tasks humans attempt. It is motivated by the desire to create an artificial reasoning system that is capable of intelligent behavior. A critical question to be answered is how is the success of this effort is to be measured and evaluated. Measurement of the outward observable system behavior, while somewhat indicative does not really measure the correctness or quality of the system's capabilities. This is especially true in complex real-time control systems such as autonomous on-road driving. This paper describes an on-going effort at NIST to do task analysis and develop performance metrics for autonomous on-road driving. This project uses the NIST Real-time Control System (RCS, now referred to as 4D/RCS) design methodology and reference architecture to develop a task decomposition representational format for the on-road driving task knowledge. This task decomposition representation is used as the framework to further specify the world model entities, attributes, features, and events required for proper reasoning about each of the subtask activities. These world model specifications, in turn, are used as the requirements for the sensory processing system. These requirements identify those things that have to be measured in the environment, including their resolutions, accuracy tolerances, detection timing, and detection distances for each subtask activity. From these can be developed a set of performance metrics that allow validation of sensory processing by evaluating the world model representations it produces for each individual component subtask activity. In this way, taxonomies of autonomous capabilities can be developed and tested against these sensory processing and world model building performance metrics. Additional metrics can be developed to measure the performance characteristics of the behavior generation component with its planning and value judgment