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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Knowledge base methodology: Methodology for first Engineering Script Language (ESL) knowledge base

The primary goal of reusing software components is that software can be developed faster, cheaper and with higher quality. Though, reuse is not automatic and can not just happen. It has to be carefully engineered. For example a component needs to be easily understandable in order to be reused, and it has also to be malleable enough to fit into different applications. In fact the software development process is deeply affected when reuse is being applied. During component development, a serious effort has to be directed toward making these components as reusable. This implies defining reuse coding style guidelines and applying then to any new component to create as well as to any old component to modify. These guidelines should point out the favorable reuse features and may apply to naming conventions, module size and cohesion, internal documentation, etc. During application development, effort is shifted from writing new code toward finding and eventually modifying existing pieces of code, then assembling them together. We see here that reuse is not free, and therefore has to be carefully managed.

Peeris, Kumar↗
NASA NTRS1995

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 54: The technical communications practices of engineering technology students: Results of the NASA/DOD Aerospace Knowledge Diffusion Research Project phase 3 student surveys

Engineering technology programs are characterized by their focus on application and practice, and by their approximately 50/50 mix of theory and laboratory experience. Engineering technology graduates are employed across the technological spectrum and are often found in areas that deal with application, implementation, and production. Yet we know very little about the communications practices and information-use skills of engineering technology students. In this paper, we report selected results of an exploratory study of engineering technology students enrolled in three U.S. institutions of higher education. Data are presented for the following topics: career goals and aspirations; the importance of, receipt of, and helpfulness of communications and information-use skills instruction; collaborative writing; use of libraries; and the use of electronic (computer) networks.

Pinelli, Thomas E.↗
NASA NTRS1991

Knowledge repositories for multiple uses

In the life cycle of a complex physical device or part, for example, the docking bay door of the Space Station, there are many uses for knowledge about the device or part. The same piece of knowledge might serve several uses. Given the quantity and complexity of the knowledge that must be stored, it is critical to maintain the knowledge in one repository, in one form. At the same time, because of quantity and complexity of knowledge that must be used in life cycle applications such as cost estimation, re-design, and diagnosis, it is critical to automate such knowledge uses. For each specific use, a knowledge base must be available and must be in a from that promotes the efficient performance of that knowledge base. However, without a single source knowledge repository, the cost of maintaining consistent knowledge between multiple knowledge bases increases dramatically; as facts and descriptions change, they must be updated in each individual knowledge base. A use-neutral representation of a hydraulic system for the F-111 aircraft was developed. The ability to derive portions of four different knowledge bases is demonstrated from this use-neutral representation: one knowledge base is for re-design of the device using a model-based reasoning problem solver; two knowledge bases, at different levels of abstraction, are for diagnosis using a model-based reasoning solver; and one knowledge base is for diagnosis using an associational reasoning problem solver. It was shown how updates issued against the single source use-neutral knowledge repository can be propagated to the underlying knowledge bases.

Williamson, Keith↗
NASA NTRS1996

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↗
NASA NTRS1992

On the acquisition and representation of procedural knowledge

Historically knowledge acquisition has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some of some types of knowledge, little attention has been devoted to procedural knowledge. NASA personnel frequently perform tasks that are primarily procedural in nature. Previous work is reviewed in the field of knowledge acquisition and then focus on knowledge acquisition for procedural tasks with special attention devoted to the Navy's VISTA tool. The design and development is described of a system for the acquisition and representation of procedural knowledge-TARGET (Task Analysis and Rule Generation Tool). TARGET is intended as a tool that permits experts to visually describe procedural tasks and as a common medium for knowledge refinement by the expert and knowledge engineer. The system is designed to represent the acquired knowledge in the form of production rules. Systems such as TARGET have the potential to profoundly reduce the time, difficulties, and costs of developing knowledge-based systems for the performance of procedural tasks.

Saito, T.↗
NASA NTRS1993

TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper.

Ortiz, C. J.↗
NASA NTRS1988

Issues on the use of meta-knowledge in expert systems

Meta knowledge is knowledge about knowledge; knowledge that is not domain specific but is concerned instead with its own internal structure. Several past systems have used meta-knowledge to improve the nature of the user interface, to maintain the knowledge base, and to control the inference engine. More extensive use of meta-knowledge is probable for the future as larger scale problems are considered. A proposed system architecture is presented and discussed in terms of meta-knowledge applications. The principle components of this system: the user support subsystem, the control structure, the knowledge base, the inference engine, and a learning facility are all outlined and discussed in light of the use of meta-knowledge. Problems with meta-constructs are also mentioned but it is concluded that the use of meta-knowledge is crucial for increasingly autonomous operations.

Facemire, Jon↗
NASA NTRS1996

Induction as Knowledge Integration

Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.

Smith, Benjamin D.↗
NASA NTRS1997

Knowledge Management

The emergence of rapidly expanding technologies for distribution and dissemination of information and knowledge has brought to focus the opportunities for development of knowledge-based networks, knowledge dissemination and knowledge management technologies and their potential applications for enhancing productivity of knowledge work. The challenging and complex problems of the future can be best addressed by developing the knowledge management as a new discipline based on an integrative synthesis of hard and soft sciences. A knowledge management professional society can provide a framework for catalyzing the development of proposed synthesis as well as serve as a focal point for coordination of professional activities in the strategic areas of education, research and technology development. Preliminary concepts for the development of the knowledge management discipline and the professional society are explored. Within this context of knowledge management discipline and the professional society, potential opportunities for application of information technologies for more effectively delivering or transferring information and knowledge (i.e., resulting from the NASA's Mission to Planet Earth) for the development of policy options in critical areas of national and global importance (i.e., policy decisions in economic and environmental areas) can be explored, particularly for those policy areas where a global collaborative knowledge network is likely to be critical to the acceptance of the policies.

Shariq, Syed Z.↗
NASA NTRS2008

The Costs of Knowledge

Acquiring knowledge-genuinely learning something new-requires the consent and commitment of the person you're trying to learn from. In contrast to information, which can usually be effectively transmitted in a document or diagram, knowledge comes from explaining, clarifying, questioning, and sometimes actually working together. Getting this kind of attention and commitment often involves some form of negotiation, since even the most generous person's time and energy are limited. Few experts sit around waiting to share their knowledge with strangers or casual acquaintances. In reasonably collaborative enterprises- I think NASA is one-this sort of negotiation isn't too onerous. People want to help each other and share what they know, so the "cost" of acquiring knowledge is relatively low. In many organizations (and many communities and countries), however, there are considerable costs associated with this activity, and many situations in which negotiations fail. The greatest knowledge cost is in and adopting knowledge to one's own use. Sometimes this means formally organizing what one learns in writing. Sometimes it means just taking time to reflect on someone else's thoughts and experiences-thinking about knowledge that is not exactly what you need but can lead you to develop ideas that will be useful. A long, discursive conversation, with all the back-and-forth that defines conversation, can be a mechanism of knowledge exchange. I have seen many participants at NASA APPEL Masters Forums talking, reflecting, and thinking-adapting what they are hearing to their own needs. Knowledge transfer is not a simple proposition. An enormous amount of information flows through the world every day, but knowledge is local, contextual, and "stickyn-that is, it takes real effort to move it from one place to another. There is no way around this. To really learn a subject, you have to work at it, you have to pay your "knowledge dues." So while, thanks to advances in technology, almost infinite amounts of information are instantly available, it still takes the same amount of time and work to learn French as it did in the year 1800-or to master physics or philosophy.

Prusak, Laurence↗
NASA NTRS2010

U.S. Spacesuit Knowledge Capture

The ability to learn from both the mistakes and successes of the past is vital to assuring success in the future. Due to the close physical interaction between spacesuit systems and human beings as users, spacesuit technology and usage lends itself rather uniquely to the benefits realized from the skillful organization of historical information; its dissemination; the collection and identification of artifacts; and the education of individuals and groups working in the field. The National Aeronautics and Space Administration (NASA), other organizations and individuals have been performing United States (U.S.) spacesuit knowledge capture since the beginning of space exploration. Avenues used to capture the knowledge have included publication of reports; conference presentations; specialized seminars; and classes usually given by veterans in the field. Recently, the effort has been more concentrated and formalized whereby a new avenue of spacesuit knowledge capture has been added to the archives through which videotaping occurs, engaging both current and retired specialists in the field presenting technical scope specifically for education and preservation of knowledge. Now with video archiving, all these avenues of learning can be brought to life with the real experts presenting their wealth of knowledge on screen for future learners to enjoy. U.S. spacesuit knowledge capture topics have included lessons learned in spacesuit technology, experience from the Gemini, Apollo, Skylab and Shuttle programs, hardware certification, design, development and other program components, spacesuit evolution and experience, failure analysis and resolution, and aspects of program management. Concurrently, U.S. spacesuit knowledge capture activities have progressed to a level where NASA, the National Air and Space Museum (NASM), Hamilton Sundstrand (HS) and the spacesuit community are now working together to provide a rather closed-looped spacesuit knowledge capture system which includes specific attention to spacesuit system artifacts as well. A NASM report has recently been created that allows the cross reference of history to the artifacts and the artifacts to the history including spacesuit manufacturing details with current condition and location. NASA has examined spacesuits in the NASM collection for evidence of wear during their operational life. NASA s formal spacesuit knowledge capture efforts now make use of both the NASM spacesuit preservation collection and report to enhance its efforts to educate NASA personnel and contribute to spacesuit history. Be it archiving of human knowledge or archiving of the actual spacesuit legacy hardware with its rich history, the joining together of spacesuit system artifact history with that of development and use during past programs will provide a wealth of knowledge which will greatly enhance the chances for the success of future and more ambitious spacesuit system programs.

Chullen, Cinda↗
NASA NTRS1989

An evidential approach to problem solving when a large number of knowledge systems is available

Some recent problems are no longer formulated in terms of imprecise facts, missing data or inadequate measuring devices. Instead, questions pertaining to knowledge and information itself arise and can be phrased independently of any particular area of knowledge. The problem considered in the present work is how to model a problem solver that is trying to find the answer to some query. The problem solver has access to a large number of knowledge systems that specialize in diverse features. In this context, feature means an indicator of what the possibilities for the answer are. The knowledge systems should not be accessed more than once, in order to have truly independent sources of information. Moreover, these systems are allowed to run in parallel. Since access might be expensive, it is necessary to construct a management policy for accessing these knowledge systems. To help in the access policy, some control knowledge systems are available. Control knowledge systems have knowledge about the performance parameters status of the knowledge systems. In order to carry out the double goal of estimating what units to access and to answer the given query, diverse pieces of evidence must be fused. The Dempster-Shafer Theory of Evidence is used to pool the knowledge bases.

Dekorvin, Andre↗
NASA NTRS1994

Increasing levels of assistance in refinement of knowledge-based retrieval systems

The task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time is discussed. In particular, the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system, is presented. DE-KART starts with knowledge that was entered manually, and increases its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge. DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.

Baudin, Catherine↗
NASA NTRS1996

Design of Composite Structures Using Knowledge-Based and Case Based Reasoning

A method of using knowledge based and case based reasoning to assist designers during conceptual design tasks of composite structures was proposed. The cooperative use of heuristics, procedural knowledge, and previous similar design cases suggests a potential reduction in design cycle time and ultimately product lead time. The hypothesis of this work is that the design process of composite structures can be improved by using Case-Based Reasoning (CBR) and Knowledge-Based (KB) reasoning in the early design stages. The technique of using knowledge-based and case-based reasoning facilitates the gathering of disparate information into one location that is easily and readily available. The method suggests that the inclusion of downstream life-cycle issues into the conceptual design phase reduces potential of defective, and sub-optimal composite structures. Three industry experts were interviewed extensively. The experts provided design rules, previous design cases, and test problems. A Knowledge Based Reasoning system was developed using the CLIPS (C Language Interpretive Procedural System) environment and a Case Based Reasoning System was developed using the Design Memory Utility For Sharing Experiences (MUSE) xviii environment. A Design Characteristic State (DCS) was used to document the design specifications, constraints, and problem areas using attribute-value pair relationships. The DCS provided consistent design information between the knowledge base and case base. Results indicated that the use of knowledge based and case based reasoning provided a robust design environment for composite structures. The knowledge base provided design guidance from well defined rules and procedural knowledge. The case base provided suggestions on design and manufacturing techniques based on previous similar designs and warnings of potential problems and pitfalls. The case base complemented the knowledge base and extended the problem solving capability beyond the existence of limited well defined rules. The findings indicated that the technique is most effective when used as a design aid and not as a tool to totally automate the composites design process. Other areas of application and implications for future research are discussed.

Lambright, Jonathan Paul↗
NASA NTRS2006

Knowledge Management: A Skeptic's Guide

A viewgraph presentation discussing knowledge management is shown. The topics include: 1) What is Knowledge Management? 2) Why Manage Knowledge? The Presenting Problems; 3) What Gets Called Knowledge Management? 4) Attempts to Rethink Assumptions about Knowledgs; 5) What is Knowledge? 6) Knowledge Management and INstitutional Memory; 7) Knowledge Management and Culture; 8) To solve a social problem, it's easier to call for cultural rather than organizational change; 9) Will the Knowledge Management Effort Succeed? and 10) Backup: Metrics for Valuing Intellectural Capital i.e. Knowledge.

Linde, Charlotte↗
NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗