The Space Object Ontology

Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining longterm space domain awareness

An Overview of the BFO - Basic Formal Ontology - and Its Applicability for Satellite Systems

This work aims to present an overview of the top-level ontology BFO - Basic Formal Ontology - and its applicability for Satellite Systems. As an upper level ontology, the BFO was designed to be extended, providing the basis for the specification of detailed representational artifacts about scientific information domains. These aspects and the challenges of satellite systems complexity and large size compose a suitable scenario for the creation of a specialized dialect to improve efficiency and accuracy when modeling such systems. By analyzing BFO based ontologies in other disciplines and existing satellite models it is possible to describe an application for satellite systems, which can provide a foundation for the creation of a concrete ontology to be applied on satellite modeling

The Space Domain Ontologies

Achieving space situational awareness requires, at a minimum, the identification, characterization, and tracking of space objects. Leveraging the resultant space object data for purposes such as hostile threat assessment, object identification, and conjunction assessment presents major challenges. This is in part because in characterizing space objects we reference a variety of identifiers, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, operational processes, operational statuses, and so forth, which tend to be defined in highly heterogeneous and sometimes inconsistent ways. The Space Domain Ontologies are designed to provide a consensus-based realist framework for formulating such characterizations in a way that is both consistent and computable. Space object data are aligned with classes and relations in a suite of ontologies built around the existing Space Object Ontology. They are stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space situational awareness and the needs of spacecraft operators and analysts. This paper provides an overview of the Space Domain Ontologies and their development and use. It presents the motivation for and advantages of the Space Domain Ontologies, including the benefits they provide for enhancing and maintaining long-term space situational awareness

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Implementing Dempster-Shafer Theory for property similarity in Conceptual Spaces modeling

Previous work has shown that the Complex Conceptual Spaces − Single Observation Mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework work, the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization

Supporting Early Mission Concept Evaluation through Natural Language Processing

Proposal evaluation of pre-Phase A mission concepts is largely based on the input from subject matter experts who determine the scientific merit of a mission concept based on a number of criteria including: the relevance of the mission objectives to national and international priorities; the existence of a complete set of measurement, instrument, and platform requirements that are traceable to the mission objectives; and several others. The Science Traceability Matrix is a standard tool used to articulate this relevance and traceability and therefore is a key input to this reviewing process. However, inconsistencies in the structure and vocabulary used in the Science Traceability Matrix and other sections of the proposal across organizations make this process challenging and time-consuming. At the same time, as part of the Digital Engineering revolution, NASA and other space organizations are starting to embrace key concepts of model-based systems engineering and understand the value of moving from unstructured text documents to more formal knowledge representations that are amenable to automated data processing. In this line, this thesis leverages transformer models, a recent advance in natural language processing, to demonstrate automatic extraction of science relevance and traceability information from unstructured mission concept proposals. By doing so, this work helps pave the way for future applications of natural language processing to support other systems engineering practices within mission/program development such as automated parsing of design documentation. The proposed tool, called AstroNLP, is evaluated with a case study based on the Astrophysics Decadal Survey

The Space Object Ontology

This paper develops the ontology of space objects for theoretical and computational ontology applied to the space (astronautical/astronomical) domain. It follows “An ontological architecture for Orbital Debris Data” (Rovetto, 2015) and “Preliminaries of a Space Situational Awareness Ontology” (Rovetto, Kelso, 2016). Important considerations for developing a space object ontology, or more broadly, a space domain ontology are presented. The main category term ‘Space Object’ is analyzed from a philosophical perspective. The ontological commitments of legal definitions for artificial space objects are also discussed. Space object taxonomies are offered and space object terms are defined