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NASA NTRS2023

U.S. Spacesuit Knowledge Capture – Expanding Our Future

NASA is going to the Moon. And it will don a new spacesuit when it reaches its destination. NASA is partnering with industry to build the spacesuit and supporting systems (i.e., surface mobility tools) that astronauts will use on the Moon, starting with Artemis III. The Johnson Space Center’s (JSC) Extravehicular Activity and Human Surface Mobility Program (EHP) is managing this effort, and the U.S. Spacesuit Knowledge Capture (SKC) Program is expanding its scope to help. For 15 years, the SKC Program has collected, archived, and disseminated decades of spacesuit-related knowledge, as appropriate, to help NASA scientists, technicians, and engineers support space exploration. The SKC Program captures its knowledge by hosting and recording subject-matter expert (SME) lectures, interviews, and workshops. It also collects retired SMEs’ reports, drawings, and schematics containing legacy spacesuit knowledge. To build a technically capable spacesuit essential for future lunar exploration, spacesuit professionals borrowed much of their knowledge from legacy Extravehicular Activity (EVA) spacesuits. Most of the SKC Program’s captured knowledge has focused on legacy and current spacesuits. To support EHP, the SKC Program is expanding its knowledge capture focus beyond the spacesuit and will seek to collect knowledge from other pertinent topics (e.g., Lunar Terrain Vehicle and EVA tools). In 2007, the SKC Program began as an independent source, without funding. As demand for capturing essential spacesuit knowledge increased, consequently, the SKC Program’s funding increased. Expansion of the SKC Program was evident in 2019, when it seized the opportunity to capture the Exploration Extravehicular Mobility Unit (xEMU) buildup at JSC. In 2021, its collaboration with the xEMU Technical Community of Practice facilitated training and knowledge sharing with the xEMU team. In 2022, the SKC Program began supporting EHP. This paper describes the SKC Program’s expansive evolution, plans to support EHP, and more.

Cinda Chullen↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition dion and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. The knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use is discussed. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. This paper discusses the knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS1985

Perspectives on knowledge in engineering design

Various perspectives are given of the knowledge currently used in engineering design, specifically dealing with knowledge-based expert systems (KBES). Constructing an expert system often reveals inconsistencies in domain knowledge while formalizing it. The types of domain knowledge (facts, procedures, judgments, and control) differ from the classes of that knowledge (creative, innovative, and routine). The feasible tasks for expert systems can be determined based on these types and classes of knowledge. Interpretive tasks require reasoning about a task in light of the knowledge available, where generative tasks create potential solutions to be tested against constraints. Only after classifying the domain by type and level can the engineer select a knowledge-engineering tool for the domain being considered. The critical features to be weighed after classification are knowledge representation techniques, control strategies, interface requirements, compatibility with traditional systems, and economic considerations.

Rasdorf, W. J.↗
NASA NTRS1990

Computer-assisted knowledge acquisition for hypermedia systems

The usage of procedural and declarative knowledge to set up the structure or 'web' of a hypermedia environment is described. An automated knowledge acquisition tool was developed that helps a knowledge engineer elicit and represent an expert's knowledge involved in performing procedural tasks. The tool represents both procedural and prerequisite, declarative knowledge that supports each activity performed by the expert. This knowledge is output and subsequently read by a hypertext scripting language to generate the link between blank, but labeled cards. Each step of the expert's activity and each piece of supporting declarative knowledge is set up as an empty node. An instructional developer can then enter detailed instructional material concerning each step and declarative knowledge into these empty nodes. Other research is also described that facilitates the translation of knowledge from one form into a form more readily useable by computerized systems.

Steuck, Kurt↗
NASA NTRS1993

Autonomously acquiring declarative and procedural knowledge for ICAT systems

The construction of Intelligent Computer Aided Training (ICAT) systems is critically dependent on the ability to define and encode knowledge. This knowledge engineering effort can be broadly divided into two categories: domain knowledge and expert or task knowledge. Domain knowledge refers to the physical environment or system with which the expert interacts. Expert knowledge consists of the set of procedures and heuristics employed by the expert in performing their task. Both these areas are a significant bottleneck in the acquisition of knowledge for ICAT systems. This paper presents a research project in the area of autonomous knowledge acquisition using a passive observation concept. The system observes an expert and then generalizes the observations into production rules representing the domain expert's knowledge.

Kovarik, Vincent J., Jr.↗
NASA NTRS2002

A Survey of Knowledge Management Research & Development at NASA Ames Research Center

This chapter catalogs knowledge management research and development activities at NASA Ames Research Center as of April 2002. A general categorization scheme for knowledge management systems is first introduced. This categorization scheme divides knowledge management capabilities into five broad categories: knowledge capture, knowledge preservation, knowledge augmentation, knowledge dissemination, and knowledge infrastructure. Each of nearly 30 knowledge management systems developed at Ames is then classified according to this system. Finally, a capsule description of each system is presented along with information on deployment status, funding sources, contact information, and both published and internet-based references.

Keller, Richard M.↗
NASA NTRSDate not supplied

U.S. Spacesuit Knowledge Capture – Creation, Curation, and Dissemination

The U.S. spacesuit is a special system that has intrigued and fascinated the world since Neil Armstrong set foot on the Moon in 1969. With over 50 years since that momentous achievement, NASA is planning to land the first woman and next man on the Moon in the near future. This goal begets the need to build a new spacesuit, a spacesuit created from the legacy knowledge of the Extravehicular Mobility Unit (EMU), combined with knowledge gained from technology development over the decades. As NASA transitions to its new horizon, the U.S. Spacesuit Knowledge Capture (SKC) Program is poised to help. The SKC Program’s primary function has been to capture, curate, and disseminate spacesuit-related knowledge among scientists, engineers, and technicians. The SKC Program was created in 2007 to capture knowledge primarily from spacesuit subject-matter experts (SMEs) who were retiring from NASA. These SMEs had 30 to 50 years of spacesuit knowledge. Over the years, the SKC Program has evolved and expanded its scope with a current focus on complementing the buildup of the Exploration EMU (xEMU) at the Johnson Space Center. As part of this focus, the SKC Program recently teamed with the xEMU Community of Practice (CoP) for knowledge sharing. The xEMU CoP provides a forum where early career engineers, professionals new to human spaceflight, and the xEMU community can come together regularly to seek guidance, share knowledge, meet their peers, discover resources, and ask questions. The CoP created an environment where the knowledge can be easily and routinely captured and recorded. The recorded events are archived and curated in an SKC Program library and disseminated as appropriate. This paper details the roles that the SKC Program and CoP play in the xEMU buildup, along with the navigation of the creation, curation, and dissemination processes.

Cinda Chullen↗
NASA NTRS1988

The elements of design knowledge capture

This paper will present the basic constituents of a design knowledge capture effort. This will include a discussion of the types of knowledge to be captured in such an effort and the difference between design knowledge capture and more traditional knowledge base construction. These differences include both knowledge base structure and knowledge acquisition approach. The motivation for establishing a design knowledge capture effort as an integral part of major NASA programs will be outlined, along with the current NASA position on that subject. Finally the approach taken in design knowledge capture for Space Station will be contrasted with that used in the HSTDEK project.

Freeman, Michael S.↗
NASA NTRS1990

Construction of dynamic stochastic simulation models using knowledge-based techniques

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM).

Williams, M. Douglas↗
NASA NTRS1990

Study of Design Knowledge Capture (DKC) schemes implemented in magnetic bearing applications

A design knowledge capture (DKC) scheme was implemented using frame-based techniques. The objective of such a system is to capture not only the knowledge which describes a design, but also that which explains how the design decisions were reached. These knowledge types were labelled definitive and explanatory, respectively. Examination of the design process helped determine what knowledge to retain and at what stage that knowledge is used. A discussion of frames resulted in the recognition of their value to knowledge representation and organization. The FORMS frame system was used as a basis for further development, and for examples using magnetic bearing design. The specific contributions made by this research include: determination that frame-based systems provide a useful methodology for management and application of design knowledge; definition of specific user interface requirements, (this consists of a window-based browser); specification of syntax for DKC commands; and demonstration of the feasibility of DKC by applications to existing designs. It was determined that design knowledge capture could become an extremely valuable engineering tool for complicated, long-life systems, but that further work was needed, particularly the development of a graphic, window-based interface.

Source record↗
NASA NTRS1994

TARGET's role in knowledge acquisition, engineering, validation, and documentation

We investigate the use of the TARGET task analysis tool for use in the development of rule-based expert systems. We found TARGET to be very helpful in the knowledge acquisition process. It enabled us to perform knowledge acquisition with one knowledge engineer rather than two. In addition, it improved communication between the domain expert and knowledge engineer. We also found it to be useful for both the rule development and refinement phases of the knowledge engineering process. Using the network in these phases required us to develop guidelines that enabled us to easily translate the network into production rules. A significant requirement for TARGET remaining useful throughout the knowledge engineering process was the need to carefully maintain consistency between the network and the rule representations. Maintaining consistency not only benefited the knowledge engineering process, but also has significant payoffs in the areas of validation of the expert system and documentation of the knowledge in the system.

Levi, Keith R.↗
NASA NTRS1993

A Knowledge-Based System Developer for aerospace applications

A prototype Knowledge-Based System Developer (KBSD) has been developed for aerospace applications by utilizing artificial intelligence technology. The KBSD directly acquires knowledge from domain experts through a graphical interface then builds expert systems from that knowledge. This raises the state of the art of knowledge acquisition/expert system technology to a new level by lessening the need for skilled knowledge engineers. The feasibility, applicability , and efficiency of the proposed concept was established, making a continuation which would develop the prototype to a full-scale general-purpose knowledge-based system developer justifiable. The KBSD has great commercial potential. It will provide a marketable software shell which alleviates the need for knowledge engineers and increase productivity in the workplace. The KBSD will therefore make knowledge-based systems available to a large portion of industry.

Shi, George Z.↗
NASA NTRS1997

Evaluation of Flight Attendant Technical Knowledge

Accident and incident reports have indicated that flight attendants have numerous opportunities to provide the flight-deck crew with operational information that may prevent or lessen the severity of a potential problem. Additionally, as carrier fleets transition from three person to two person flight-deck crews, the reliance upon the cabin crew for the transfer of this information may increase further. Recent research indicates that flight attendants do not feel confident in their ability to describe mechanical parts or malfunctions of the aircraft, and the lack of flight attendant technical training has been referenced in a number of recent reports. Chute and Wiener describe five factors which may produce communication barriers between cockpit and cabin crews: the historical background of aviation, the physical separation of the two crews, psychosocial issues, regulatory factors, and organizational factors. By examining these areas of division we can identify possible bridges and address the implications of deficient cockpit/cabin communication on flight safety. Flight attendant operational knowledge may provide some mitigation of these barriers. The present study explored both flight attendant technical knowledge and flight attendant and pilot expectations of flight attendant technical knowledge. To assess the technical knowledge of cabin crewmembers, 177 current flight attendants from two U.S. carriers voluntarily completed a 13-item technical quiz. To investigate expectations of flight attendant technical knowledge, 181 pilots and a second sample of 96 flight attendants, from the same two airlines, completed surveys designed to capture each group's expectations of operational knowledge required of flight attendants. Analyses revealed several discrepancies between the present level of flight attendant operational knowledge and pilots' and flight attendants' expected and desired levels of technical knowledge. Implications for training will be discussed.

Dunbar, Melisa G.↗
NASA NTRS1999

A Discussion of Knowledge Based Design

A discussion of knowledge and Knowledge- Based design as related to the design of aircraft is presented. The paper discusses the perceived problem with existing design studies and introduces the concepts of design and knowledge for a Knowledge- Based design system. A review of several Knowledge-Based design activities is provided. A Virtual Reality, Knowledge-Based system is proposed and reviewed. The feasibility of Virtual Reality to improve the efficiency and effectiveness of aerodynamic and multidisciplinary design, evaluation, and analysis of aircraft through the coupling of virtual reality technology and a Knowledge-Based design system is also reviewed. The final section of the paper discusses future directions for design and the role of Knowledge-Based design.

Wood, Richard M.↗
NASA NTRS2006

Marshall Space Flight Center Propulsion Systems Department (PSD) Knowledge Management (KM) Initiative

NASA Marshall Space Flight Center's Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities within the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center of Excellence (AISCE), lntergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KNI implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to suppo~th e planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have beon pedormed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural1KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level svstems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, - evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.

Caraccioli, Paul↗