The Ontology Action Team (OAT) is part of the INCOSE Model-Based System Engineering (MBSE) initiative. The OAT goal is to leverage ontology practices to improve MBSE. In particular, Ontology is an enabler of good modeling in that it focuses on establishing well defined domain concepts in terms of the terminology, definitions, and relationships as needed to model real world applications. In addition, the use of formal semantics is essential for modeling languages to properly represent the concepts, and to enable additional analysis to be performed about the systems. The combination of a controlled vocabulary and underlying formalism provides the opportunity to create more consistent models and improve semantic interoperability. Ontologies have been developed and are in use in many areas of science (biomedical, chemical, neuroscience), business, and engineering. These ontologies and the lessons learned in their development are a rich source material for the OAT effort.
The OAT maintains this wiki to coordinate the OAT activities which include performing outreach to communities with overlapping interests and providing links to relevant resources.
The purpose of OAT is to facilitate the incorporation of ontological methods in MBSE and the exchange of ideas in this area. Individuals with interest in ontology in MBSE are encouraged to participate in the development of this site and to help identify material that should be included. The success criteria for OAT is:
Model-based systems engineering (MBSE) is the formalized application of system modeling for requirements, design, analysis, verification, and validation. Modeling begins in the conceptual design phase and continues throughout product development and later life cycle phases. In MBSE, models are used as requirements and design specifications. In the INCOSE strategic vision, models replace documents as the primary product or artifact of the system engineering process. Success of the vision requires a firm mathematical foundation for real world representations and high fidelity simulation with a unified methodology that produces high quality, reusable models. Standards for developing, sharing, and managing models are required to achieve this vision.
Ontology as a philosophical discipline aims at developing a system of general categories, the relationships between them and the rules that govern them which together form a theory of reality. How does ontology relate to the practice of building models in an engineering modeling language such as SysML or ISO15926? Models are used to describe systems that exist or might exist in the real world. An ontology provides the concepts used to describe the real world and their meaning. Use of standardized ontologies, which make concepts precise, enables better model sharing. The development of system engineering ontologies that can be used as libraries will facilitate the ability to quickly develop models and to make their sharing easier.
Collaboration and sharing of models requires that models use common terminology and the terminology has well-defined meaning. For collaboration, the meaning of the models needs to be expressed without the models having to be accompanied by subject domain experts. For concepts like part of and product version, the informal meaning and even natural language definitions within standards are not sufficiently precise to rule out different interpretations for the same term. A formal semantics for a modeling language enables a precision of standardization not possible with syntactic and data interchange formats. A formal semantics is required to make ontology meaning precise as well as making the meaning of models precise.
Of particular interest to OAT is the integration of reasoning with modeling. In order to draw valid conclusions from models automatically, computer programs require an expressive modeling (ontology) language with ontology standards (concepts used in the models). The language must have an inference rule semantics that can be used for automated reasoning that is in accord with the intended meaning of the models. The establishment of a recognized formal semantics for modeling languages can enable integrated automated reasoning into the development process. Automated reasoning can mitigate engineering tasks that are currently manual, error prone, and time consuming. Models are used to perform analysis and answer questions. The complexity of product development has outstripped the capability to manually perform analysis and answer questions.
OAT success is to be measured against the OAT success criteria. The success is incremental. This section of the Wiki will be updated as evidence is obtained. Evidence of success for each of the criteria are described below.
Evidence of ontology use means not just that practitioners use the term ontology, but there is documented use of methodology and conceptualizations that are part of the ontology literature.
Develop and identifying knowledge engineering principles for constructing ontologies
The objective is to identify research which evaluates the suitability of engineering languages for representing ontologies and for integrating with reasoning.\\
|Paper on integrating reasoning with SysML||integration_15-mar-12.pdf|
Develop a taxonomy of the ontologies needed for system engineering
The objective is to provide links to publically available ontologies that might be candidates for standardization.
The objective is to document research on the formal semantics of engineering languages particularly as regards to representing ontologies.
Provide outreach to the engineering community on the value of ontological standards for MBSE and the results obtained by the OAT
|Henson Graves||Algos Associatesemail@example.com|
|Yvonne Bijan||Lockheed Martinfirstname.lastname@example.org|
|Allison Barnard Feeney||NISTemail@example.com|
Ontology Working Group Plan
Background Material for MBSE Ontology Development
NIST Investigation - Semantics for SysEng and ILS
Activity Area Results
Link to related items such as related organizations, related articles, …