Semantically-Rigorous Systems Engineering Using SysML and OWL
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software architecture↗
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ODISEES: Ontology-Driven Interactive Search Environment for Earth Sciences
This paper discusses the Ontology-driven Interactive Search Environment for Earth Sciences (ODISEES) project currently being developed to aid researchers attempting to find usable data among an overabundance of closely related data. ODISEES' ontological structure relies on a modular, adaptable concept modeling approach, which allows the domain to be modeled more or less as it is without worrying about terminology or external requirements. In the model, variables are individually assigned semantic content based on the characteristics of the measurements they represent, allowing intuitive discovery and comparison of data without requiring the user to sift through large numbers of data sets and variables to find the desired information.
Rutherford, Matthew T.↗
A Hybrid Approach to Labeling Datasets in Earth Science Publications
NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.
Jacob Atkins↗
Relating MBSE to Spacecraft Development: A NASA Pathfinder
The NASA Engineering and Safety Center (NESC) has sponsored a Pathfinder Study to investigate how Model Based Systems Engineering (MBSE) and Model Based Engineering (MBE) techniques can be applied by NASA spacecraft development projects. The objectives of this Pathfinder Study included analyzing both the products of the modeling activity, as well as the process and tool chain through which the spacecraft design activities are executed. Several aspects of MBSE methodology and process were explored. Adoption and consistent use of the MBSE methodology within an existing development environment can be difficult. The Pathfinder Team evaluated the possibility that an "MBSE Template" could be developed as both a teaching tool as well as a baseline from which future NASA projects could leverage. Elements of this template include spacecraft system component libraries, data dictionaries and ontology specifications, as well as software services that do work on the models themselves. The Pathfinder Study also evaluated the tool chain aspects of development. Two chains were considered: 1. The Development tool chain, through which SysML model development was performed and controlled, and 2. The Analysis tool chain, through which both static and dynamic system analysis is performed. Of particular interest was the ability to exchange data between SysML and other engineering tools such as CAD and Dynamic Simulation tools. For this study, the team selected a Mars Lander vehicle as the element to be designed. The paper will discuss what system models were developed, how data was captured and exchanged, and what analyses were conducted.
Othon, Bill↗
Smarter Earth Science Data System
The explosive growth in Earth observational data in the recent decade demands a better method of interoperability across heterogeneous systems. The Earth science data system community has mastered the art in storing large volume of observational data, but it is still unclear how this traditional method scale over time as we are entering the age of Big Data. Indexed search solutions such as Apache Solr (Smiley and Pugh, 2011) provides fast, scalable search via keyword or phases without any reasoning or inference. The modern search solutions such as Googles Knowledge Graph (Singhal, 2012) and Microsoft Bing, all utilize semantic reasoning to improve its accuracy in searches. The Earth science user community is demanding for an intelligent solution to help them finding the right data for their researches. The Ontological System for Context Artifacts and Resources (OSCAR) (Huang et al., 2012), was created in response to the DARPA Adaptive Vehicle Make (AVM) programs need for an intelligent context models management system to empower its terrain simulation subsystem. The core component of OSCAR is the Environmental Context Ontology (ECO) is built using the Semantic Web for Earth and Environmental Terminology (SWEET) (Raskin and Pan, 2005). This paper presents the current data archival methodology within a NASA Earth science data centers and discuss using semantic web to improve the way we capture and serve data to our users.
data center↗
An Ontology for State Analysis: Formalizing the Mapping to SysML
State Analysis is a methodology developed over the last decade for architecting, designing and documenting complex control systems. Although it was originally conceived for designing robotic spacecraft, recent applications include the design of control systems for large ground-based telescopes. The European Southern Observatory (ESO) began a project to design the European Extremely Large Telescope (E-ELT), which will require coordinated control of over a thousand articulated mirror segments. The designers are using State Analysis as a methodology and the Systems Modeling Language (SysML) as a modeling and documentation language in this task. To effectively apply the State Analysis methodology in this context it became necessary to provide ontological definitions of the concepts and relations in State Analysis and greater flexibility through a mapping of State Analysis into a practical extension of SysML. The ontology provides the formal basis for verifying compliance with State Analysis semantics including architectural constraints. The SysML extension provides the practical basis for applying the State Analysis methodology with SysML tools. This paper will discuss the method used to develop these formalisms (the ontology), the formalisms themselves, the mapping to SysML and approach to using these formalisms to specify a control system and enforce architectural constraints in a SysML model.
Wagner, David A.↗
Theory and ontology for sharing temporal knowledge
Using current technology, the sharing or re-using of knowledge-bases is very difficult, if not impossible. ARPA has correctly recognized the problem and funded a knowledge sharing initiative. One of the outcomes of this project is a formal language called Knowledge Interchange Format (KIF) for representing knowledge that could be translated into other languages. Capturing and representing design knowledge and reasoning with them have become very important for NASA who is a pioneer of innovative design of unique products. For upgrading an existing design for changing technology, needs, or requirements, it is essential to understand the design rationale, design choices, options and other relevant information associated with the design. Capturing such information and presenting them in the appropriate form are part of the ongoing Design Knowledge Capture project of NASA. The behavior of an object and various other aspects related to time are captured by the appropriate temporal knowledge. The captured design knowledge will be represented in such a way that various groups of NASA who are interested in various aspects of the design cycle should be able to access and use the design knowledge effectively. To facilitate knowledge sharing among these groups, one has to develop a very well defined ontology. Ontology is a specification of conceptualization. In the literature several specific domains were studied and some well defined ontologies were developed for such domains. However, very little, or no work has been done in the area of representing temporal knowledge to facilitate sharing. During the ASEE summer program, I have investigated several temporal models and have proposed a theory for time that is flexible to accommodate the time elements, such as, points and intervals, and is capable of handling the qualitative and quantitative temporal constraints. I have also proposed a primitive temporal ontology using which other relevant temporal ontologies can be built. I have investigated various issues of sharing knowledge and have proposed a formal framework for modeling the concept of knowledge sharing. This work may be implemented and tested in the software environment supplied by Knowledge Based System, Inc.
Loganantharaj, Rasiah↗
INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY
Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.
informatics↗
INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY
Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods.
knowledge↗
NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility
NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship.
Life Sciences data↗
ATOS-1: Designing the infrastructure for an advanced spacecraft operations system
The space industry has identified the need to use artificial intelligence and knowledge based system techniques as integrated, central, symbolic processing components of future mission design, support and operations systems. Various practical and commercial constraints require that off-the-shelf applications, and their knowledge bases, are reused where appropriate and that different mission contractors, potentially using different KBS technologies, can provide application and knowledge sub-modules of an overall integrated system. In order to achieve this integration, which we call knowledge sharing and distributed reasoning, there needs to be agreement on knowledge representations, knowledge interchange-formats, knowledge level communications protocols, and ontology. Research indicates that the latter is most important, providing the applications with a common conceptualization of the domain, in our case spacecraft operations, mission design, and planning. Agreement on ontology permits applications that employ different knowledge representations to interwork through mediators which we refer to as knowledge agents. This creates the illusion of a shared model without the constraints, both technical and commercial, that occur in centralized or uniform architectures. This paper explains how these matters are being addressed within the ATOS program at ESOC, using techniques which draw upon ideas and standards emerging from the DARPA Knowledge Sharing Effort. In particular, we explain how the project is developing an electronic Ontology of Spacecraft Operations and how this can be used as an enabling component within space support systems that employ advanced software engineering. We indicate our hope and expectation that the core ontology developed in ATOS, will permit the full development of standards for such systems throughout the space industry.
Poulter, K. J.↗
Ontology Development and Evolution in the Accident Investigation Domain
InvestiigationOrganizer (IO) is a collaborative semantic web system designed to support the conduct of mishap investigations. IO provides a common repository for a wide range of mishap related information, allowing investigators to integrate evidence, causal models, and investigation results. IO has been used to support investigations ranging from a small property damage case to the loss of the Space Shuttle Columbia. Through IO'S use in these investigations, we have learned significant lessons? about the application of ontologies and semantic systems to solving real-world problems. This paper will describe the development of the ontology within IO, from the initial development, its growth in response to user requests during use in investigations, and the recent work that was done to control the results of that growth. This paper will also describe the lessons learned from this experience and how they may apply to the implementaton of future ontologies and semantic systems.
Carvalho, Robert↗
SmallSat / Model-Based Mission Assurance Session Overview
SmallSat state of the art report advanced avionics section updates. Systematic CubeSat radiation hardening assurance using the SEAM platforms. Building ontologies for AI-integrated prognostic health management of spacecraft avionics.
Rebekah Austin↗
NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility
NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.
Life Sciences data↗
Command Center Training Tool (C2T2)
This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.
Jones, Phillip↗
Can the Hypothesis 'Photon Interferes only with Itself' be Reconciled with Superposition of Light from Multiple Beams or Sources?
Any superposition effect as measured (SEM) by us is the summation of simultaneous stimulations experienced by a detector due to the presence of multiple copies of a detectee each carrying different values of the same parameter. We discus the cases with light beams carrying same frequency for both diffraction and multiple beam Fabry-Perot interferometer and also a case where the two superposed light beams carry different frequencies. Our key argument is that if light really consists of indivisible elementary particle, photon, then it cannot by itself create superposition effect since the state vector of an elementary particle cannot carry more than one values of any parameter at the same time. Fortunately, semiclassical model explains all light induced interactions using quantized atoms and classical EM wave packet. Classical physics, with its deeper commitment to Reality Ontology, was better prepared to nurture the emergence of Quantum Mechanics and still can provide guidance to explore nature deeper if we pay careful attention to successful classical formulations like Huygens-Fresnel diffraction integral.
Roychoudhuri, Chandrasekhar↗
Predicting Crew Time Allocations for Lunar Orbital Missions Based on Historical ISS Operational Activities
As the National Aeronautics and Space Administration continues to define candidate architectures for the planned lunar “Gateway”, it will be necessary to have a detailed understanding of how the crew will inhabit, operate, and maintain the spacecraft. The nature of the Gateway vehicle systems configuration and operations will have a direct impact on the scope of work activities required of the crew. Crew work schedules are sensitive to variations in spacecraft architecture, visiting vehicle activities, and logistics operations – particularly within short duration missions as initially planned for the lunar Gateway. These system and operational configurations must be taken into account when planning for crew time availability to conduct science activities on Gateway missions. This paper presents a methodology that is used to predict crew time distributions for lunar Gateway missions, as applied in NASA’s Exploration Crew Time Model (ECTM). The process utilized for evaluating crew time distributions is based on the categorization of all crew activities into a standardized ontology. Historical ISS daily crew timeline data from July 20, 2011 (post STS retirement) to present day was captured via the Operational Planning Timeline Integration System (OPTimIS) database and characterized according to the standardized ontology. This process enabled correlation and statistical analysis of the ISS data according to common mission parameters such as crew size, ECLSS system design, vehicle traffic operations, and logistics delivery operations. The results of the statistical analysis are a set of crew time distributions for each activity category. These distributions are then utilized within the ECTM to examine crew time allocations based on mission parameter inputs, which serve to characterize the Gateway mission configurations. Results for predicted crew time allocations for representative short duration Gateway missions are presented. These results can be used to evaluate crew schedule availability for science and utilization activities. Variations in expected mission architectures and mission operations are accounted for to correct crew time predictions. The analysis is being leveraged to plan utilization capability objectives that are achievable on the Gateway missions, as well as inform the viability of various mission architecture options.
Stromgren, Chel↗