In this paper, we introduce a system to collect product information from manufacturers and make it available in tools that are used for concurrent design of spacecraft. The planning of a spacecraft needs experts from different disciplines, like propulsion, power, and thermal. Since these different disciplines rely on each other there is a high need for communication between them, which is often realized by a Model-Based Systems Engineering (MBSE) process and corresponding tools. We show by comparison that the product information provided by manufacturers often does not match the information needed by MBSE tools on a syntactic or semantic level. The information from manufacturers is also currently not available in machine-readable formats. Afterwards, we present a prototype of a system that makes product information from manufacturers directly available in MBSE tools, in a machine-readable way.
Engineering phase specific tailored views on data are required to prevent over-engineering and to discover missing elements. This paper presents an approach to enhance a semantic Conceptual Data Model (CDM) to define the specific behavior of engineering phases. Therefore, a CDM gets enhanced by an ontology profile to specify required and non-admissible features for each engineering phase. This is exploited to enhance exis­ting transformations from Web Ontology Language (OWL) to Eclipse Modeling Language (EMF) to generate phase specific decorators representing the corresponding views on data items. The approach also supports engineering phase specific handling of constraints and rules that further augment the guidance of engineers through the system development process by applying knowledge management functions such as reasoning. As a result, the presented approach reveals a notably higher data quality and offers more analysis potential by enabling phase-specific definitions of views on data and specification of semantic checks on CDM level. Several use cases from aerospace engineering have been analyzed and the improvements in areas such as inconsistency detection, knowledge derivation, and guided system modeling are highlighted in this paper. The development of increasingly complex systems with improved quality levels becomes more and more challenging. Engineering data frameworks with integrated system models have been developed to manage such systems. This paper presents the experiences that have been made in digital systems engineer­ing in the aerospace domain and focuses on the roadmap that has been taken to establish a knowledge engineering framework. While working with first versions of these tools, it became obvious that an engineering framework reflecting all aspects of an engineering data object was required. In addition, data analytics and technologies used to check data consistency became increasingly relevant. As a consequence, semantically rich data models expressed by ontologies come into focus of forming the engineering framework baseline in conjunction with related technologies such as reasoning, error avoidance based on data analytics, and knowledge-driven engineering environments.  The engineering of complex systems is more and more supported through computer-based models that rely on a comprehensive specification of their underlying data. This paper reflects on extensive industrial experience with a sophisticated application of conceptual data modeling, addressing requirements as they arise in the context of space systems engineering. For this purpose identified needs for conceptual data modeling in the scope of Model-Based Systems Engineering are formulated. Established and evolving approaches and technologies for building conceptual data models are characterized, analyzed, and discussed regarding their suitability for modeling engineering data. Based on this analysis of the state of the art, recommendations for the future evolution of conceptual data modeling are formulated. Ontologies have been increasingly used over the past few decades in a wide range of application domains spanning both academic and industrial communities. As ontologies are the cornerstone of the Semantic Web, the technologies developed in this context, including ontology languages, specialized databases and query languages, have become widely used. However, the expressiveness of the proposed ontology languages does not always cover the needs of specific domains. For instance, engineering is a domain for which the LIAS laboratory has proposed dedicated solutions with a worldwide recognition. The underlying assumptions made in the context of the Semantic Web, an open and distributed environment, do not apply to the controlled environments of our projects where the correctness and completeness of modeling can be guaranteed to a certain degree. As a consequence, we have developed over the last decades a specialized standard ontology language named PLIB associated with the OntoDB OntoQL platform to manage ontological engineering data within a database. The goal of this paper is threefold: (1) to share our experience in manipulating ontologies in the engineering domain by describing their specificities and constraints; (2) to define a comprehensive classification of ontologies with respect to three main research communities: Artificial Intelligence, Databases and Natural Language Processing and (3) to present a persistent solution, called OntoDB, for managing extremely large semantic data sets associated with an ontological query language, called OntoQL. These objectives are illustrated by several examples that show the effectiveness and interest of our propositions in several industrial projects in different domains including vehicle manufacturing and CO (_2 ) storage. This paper presents the design and usage of a language for Conceptual Data Modeling in Model-based Systems Engineering. Based on an existing analysis of presently employed data modeling languages, a new conceptual data modeling language is defined that brings together characteristic features from software engineering languages, features from languages classically employed for knowledge engineering, as well as entirely newly developed functional aspects. This language has been applied to model a spacecraft as an example, demonstrating its utility for developing complex, multidisciplinary systems in the scope of Model-based Space Systems Engineering. Model-based Systems Engineering requires an intuitive semantically strong data model to enable precise data specification and provide the foundation for fruitful data analyses during data evolution. This paper presents an approach to use the Web Ontology Language (OWL) for specifying a Conceptual Data Model (CDM) being transformed into a format understandable by the Eclipse Modeling Framework (EMF) to profit from powerful data handling and knowledge management functions during runtime. Coalescing OWL with EMF brings up the strength of both approaches leading to considerably better data models with less failure potential and reveal notably more analysis potential by using a common data model specification. This approach also enables the direct application of reasoning functionality for automatic inference of several pieces of knowledge and automatic checks as illustrated by examples from aerospace industry. In model-based systems engineering a model specifying the system’s design is shared across a variety of disciplines and used to ensure the consistency and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the application of an ontology for space system design that provides increased semantic soundness of the underlying standardized data specification, enables reasoners to identify problems in the system, and allows the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design project, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued. 