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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Semantic Search with Sentence-BERT for Design Information Retrieval

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when finetuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

Hannah S. Walsh↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
NASA NTRS1991

Automated information retrieval using CLIPS

Expert systems have considerable potential to assist computer users in managing the large volume of information available to them. One possible use of an expert system is to model the information retrieval interests of a human user and then make recommendations to the user as to articles of interest. At Cal Poly, a prototype expert system written in the C Language Integrated Production System (CLIPS) serves as an Automated Information Retrieval System (AIRS). AIRS monitors a user's reading preferences, develops a profile of the user, and then evaluates items returned from the information base. When prompted by the user, AIRS returns a list of items of interest to the user. In order to minimize the impact on system resources, AIRS is designed to run in the background during periods of light system use.

Raines, Rodney Doyle, III↗
NASA NTRS2003

Software Helps Retrieve Information Relevant to the User

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

Mathe, Natalie↗
NASA NTRS2023

BERT-Based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

Hazard analysis↗
NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗
NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗
NASA NTRS1991

A vector-product information retrieval system adapted to heterogeneous, distributed computing environments

Vector-product information retrieval (IR) systems produce retrieval results superior to all other searching methods but presently have no commercial implementations beyond the personal computer environment. The NASA Electronic Library Systems (NELS) provides a ranked list of the most likely relevant objects in collections in response to a natural language query. Additionally, the system is constructed using standards and tools (Unix, X-Windows, Notif, and TCP/IP) that permit its operation in organizations that possess many different hosts, workstations, and platforms. There are no known commercial equivalents to this product at this time. The product has applications in all corporate management environments, particularly those that are information intensive, such as finance, manufacturing, biotechnology, and research and development.

Rorvig, Mark E.↗
NASA NTRS1993

Coordinating Council. Tenth Meeting: Information retrieval: The role of controlled vocabularies

The theme of this NASA Scientific and Technical Information Program Coordinating Council meeting was the role of controlled vocabularies (thesauri) in information retrieval. Included are summaries of the presentations and the accompanying visuals. Dr. Raya Fidel addressed 'Retrieval: Free Text, Full Text, and Controlled Vocabularies.' Dr. Bella Hass Weinberg spoke on 'Controlled Vocabularies and Thesaurus Standards.' The presentations were followed by a panel discussion with participation from NASA, the National Library of Medicine, the Defense Technical Information Center, and the Department of Energy; this discussion, however, is not summarized in any detail in this document.

Source record↗
NASA NTRS1993

An integrated information retrieval and document management system

This paper describes the requirements and prototype development for an intelligent document management and information retrieval system that will be capable of handling millions of pages of text or other data. Technologies for scanning, Optical Character Recognition (OCR), magneto-optical storage, and multiplatform retrieval using a Standard Query Language (SQL) will be discussed. The semantic ambiguity inherent in the English language is somewhat compensated-for through the use of coefficients or weighting factors for partial synonyms. Such coefficients are used both for defining structured query trees for routine queries and for establishing long-term interest profiles that can be used on a regular basis to alert individual users to the presence of relevant documents that may have just arrived from an external source, such as a news wire service. Although this attempt at evidential reasoning is limited in comparison with the latest developments in AI Expert Systems technology, it has the advantage of being commercially available.

Coles, L. Stephen↗
NASA NTRS1974

A study of the relative effectiveness and cost of computerized information retrieval in the interactive mode

Results of a number of experiments to illuminate the relative effectiveness and costs of computerized information retrieval in the interactive mode are reported. It was found that for equal time spent in preparing the search strategy, the batch and interactive modes gave approximately equal recall and relevance. The interactive mode however encourages the searcher to devote more time to the task and therefore usually yields improved output. Engineering costs as a result are higher in this mode. Estimates of associated hardware costs also indicate that operation in this mode is more expensive. Skilled RECON users like the rapid feedback and additional features offered by this mode if they are not constrained by considerations of cost.

Smetana, F. O.↗
NASA NTRS2001

Information Retrieval from SAGE II and MFRSR Multi-Spectral Extinction Measurements

Direct beam spectral extinction measurements of solar radiation contain important information on atmospheric composition in a form that is essentially free from multiple scattering contributions that otherwise tend to complicate the data analysis and information retrieval. Such direct beam extinction measurements are available from the solar occultation satellite-based measurements made by the Stratospheric and Aerosol Gas Experiment (SAGE II) instrument and by ground-based Multi-Filter Shadowband Radiometers (MFRSRs). The SAGE II data provide cross-sectional slices of the atmosphere twice per orbit at seven wavelengths between 385 and 1020 nm with approximately 1 km vertical resolution, while the MFRSR data provide atmospheric column measurements at six wavelengths between 415 and 940 nm but at one minute time intervals. We apply the same retrieval technique of simultaneous least-squares fit to the observed spectral extinctions to retrieve aerosol optical depth, effective radius and variance, and ozone, nitrogen dioxide, and water vapor amounts from the SAGE II and MFRSR measurements. The retrieval technique utilizes a physical model approach based on laboratory measurements of ozone and nitrogen dioxide extinction, line-by-line and numerical k-distribution calculations for water vapor absorption, and Mie scattering constraints on aerosol spectral extinction properties. The SAGE II measurements have the advantage of being self-calibrating in that deep space provides an effective zero point for the relative spectral extinctions. The MFRSR measurements require periodic clear-day Langley regression calibration events to maintain accurate knowledge of instrument calibration.

Lacis, Andrew A.↗
NASA NTRSDate not supplied

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Machine Learning↗
NASA NTRSDate not supplied

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Systems Engineering↗
NASA NTRS1982

Langley Atmospheric Information Retrieval System (LAIRS): System description and user's guide

This document presents the user's guide, system description, and mathematical specifications for the Langley Atmospheric Information Retrieval System (LAIRS). It also includes a description of an optimal procedure for operational use of LAIRS. The primary objective of the LAIRS Program is to make it possible to obtain accurate estimates of atmospheric pressure, density, temperature, and winds along Shuttle reentry trajectories for use in postflight data reduction.

Boland, D. E., Jr.↗