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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.

Jia Zhang↗
NASA NTRS2016

Topic Modeling of NASA Space System Problem Reports: Research in Practice

Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires nontrivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless,topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.

Data Mining↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A Covid-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRSDate not supplied

Augmenting Topic Finding in the NASA Aviation Safety Reporting System using Topic Modeling

Context: The NASA Aviation and Safety Reporting System (ASRS) provides various publications to the aviation community (including individual anonymous reports, Callback, Database Search Requests, Directline, and Alerting Messages). Key to these publications are the timely processing of new reports, which is currently done mostly manually by ASRS staff, and which the volume increases yearly. Aim: We investigate whether existing topic modelling techniques are suitable to ease some of the manual effort, and to enhance it with additional visual cues regarding the process of grouping, sense making and labeling incoming (and previous) reports. Method: We evaluate the applicability of WarpLDA topic modelling results combined with three visualization tools, the first two of which have been extended by us in this work for ASRS: Termite, TopicFlow, and LDAVis. Based on the identified limitations in these tools, we propose a methodology for improving them, and evaluate their outputs using ASRS as our test dataset. Results: The user interfaces of Termite, Topicflow and LDAVis were found insufficient for sense-making of the narratives. Moreover, concerns regarding the stability of results due to the inherent randomness of topic modelling, and the lack of a measurable approach for evaluation against the existing ASRS manual workflow were also noted. Conclusion: While many tools to topic modeling and visualization have been proposed, more work is necessary before they can be applied in practical situations to improve existing manual workflows. The methodology presented and applied in this work contribute towards this effort.

ASRS↗
NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS Using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

Text Mining↗
NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS using Seeds for Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

LDA↗
NASA NTRSDate not supplied

Assessing the use of UAS-related terms in ASRS using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database. Augmenting the discovery of topics of user interest in this online database would therefore be beneficial to the community it serves. Aim: We propose and execute an experiment to assess the use of seed term topic modeling using the database narratives to identify UAS reports. The use of seed term topic modeling would enable users to identify groups of related narratives associated to a topic of their interest. Method: We use a newly curated field in ASRS reports which identify UAS from non-UAS reports in combination of different set of UAS related terms to assess if seed topic modeling can be used in ASRS.

LDA↗
NASA NTRS2021

Topic Modeling Tool for PeTaL (Periodic Table of Life)

A topic modeling tool is constructed for the purpose of providing insights from biology to the engineer within the framework of PeTaL (Periodic Table of Life). The machine learning text mining tools–latent Dirichlet allocation (LDA) and nonnegative matrix factorization (NMF) with Kullback-Leibler (KL) divergence—are used to provide topic clusters to the user. Topic clusters are the underlying themes of a paper. For the text modeling problem, NMF-KL is the equivalent of probabilistic latent semantic analysis. Both LDA and NMF-KL are top-performing modeling tools. These tools are used to identify biological specimens relevant to the user. Various organisms solve a particular survival problem in nature differently. The topic clusters allow people without domain expertise to find these cross-topic themes in the body of documents and then branch out and examine papers whose target organisms solve the engineer’s problem. Abstracts from the Journal of Experimental Biology were used as input for the clustering tool in addition to a curated set of articles for validation. The tool is able to accept alternate input sources.

Machine learning↗
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 NTRSDate not supplied

Exploring the Landscape of Earth and Space Science Informatics using Latent Topic Modeling

AGU Earth and Space Science Informatics (ESSI) is at the forefront of data management, analysis, large scale experimentation, and infrastructure development pertaining to Earth and Space Science interests. The key topics of interest within ESSI are also evolving and diversifying over time. We aim to observe and quantify the various topics covered in ESSI, analyze their trends over time, and identify the contributors’ affiliations to gain an understanding of the landscape of ESSI and the direction of the research and management. The data for this work are abstracts submitted to AGU’s ESSI Fall meeting; They serve as a proxy for key research and development areas within ESSI. We use an unsupervised topic modeling technique called Latent Dirichlet Allocation to observe the underlying topics covered in ESSI and their trends over time. With this presentation, we showcase our results from the analysis and insights gained.

Muthukumaran Ramasubramanian↗
NASA NTRSDate not supplied

A Survey Protocol to Assess Meaningfulness and Usefulness of Automated Topic Finding in the NASA Aviation Safety Reporting System

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database, Safety Alert Bulletins and For Your Information Notices, and the CALLBACK newsletter. Key to these publications are the timely processing (de-identification, coding and summarization) of new reports, which is currently done by ASRS expert safety analysts. Thus, we believe topic modelling could decrease effort in ASRS, if topics are comprehensible. Aim: We propose a methodology to evaluate whether automated topic finding using topic modelling provides meaningful and useful topics. Method: We extend the total error survey methodology to evaluate user topic comprehension of machine learning outputs. To accomplish this we performed a literature review to identify existing methods and define a construct for topic comprehension, utilizing existing ASRS synopsis writing practices to more precisely define meaningfulness and usefulness. Results: A survey protocol was created that addresses the limitations of other survey protocols found in the literature review, which we found lacking in rationale and clear protocol definition. Conclusion: The surveying of user understanding in machine learning outputs presents challenges due to the explosion of parameters to control for and the lack of systematic approach presented in the literature. More reproducible work and survey protocols are needed in the literature and our work is one step towards that direction.

topic finding↗
NASA NTRS2000

A Software Risk Model

Topics on the slide include:quality, test coverage, functional criticality, exposure, criticality, and risk index.

Lee, Alice↗
NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗
NASA NTRS1991

Improved large perturbation propulsion models for control system design (1988-1989) and large perturbation models of high velocity propulsion systems (1989-1990) and reduced order propulsion models for control system design (1990-1991)

Methods for modeling high speed propulsion systems will be discussed. Included in this category are internal flow propulsion systems without rotating machinery, such as inlets, ramjets, and scramjets. Among the modeling topics discussed are modeling of linear isentropic flow, heat exchange, gasdynamics, lumped parameter systems, and infinite dimensional systems. Furthermore, a generalized overview of modeling high speed propulsion systems is presented in this collection of papers.

Hartley, Tom T.↗