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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Extracting text from real-world scenes

Many scenes contain significant textual information that can be extremely helpful for understanding and/or navigation. For example, text-based information can frequently be the primary cure used for navigating inside buildings. A subject might first read a marquee, then look for an appropriate hallway and walk along reading door signs and nameplates until the destination is found. Optical character recognition has been studied extensively in recent years, but has been applied almost exclusively to printed documents. As these techniques improve it becomes reasonable to ask whether they can be applied to an arbitrary scene in an attempt to extract text-based information. Before an automated system can be expected to navigate by reading signs, however, the text must first be segmented from the rest of the scene. This paper discusses the feasibility of extracting text from an arbitrary scene and using that information to guide the navigation of a mobile robot. Considered are some simple techniques for first locating text components and then tracking the individual characters to form words and phrases. Results for some sample images are also presented.

Bixler, J. Patrick↗
DOE OSTI2020

Figure Descriptive Text Extraction Using Ontological Representation

Abstract Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete, and more descriptions reside in body text. This work presents a method to extract figure descriptive text from the body of scientific articles. We adopted ontological semantics to aid concept recognition of figure-related information, which generates human- and machine-readable knowledge representations from sentences. Our results show that conceptual models bring an improvement in figure descriptive sentence classification over word-based approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Marve: Measurement Context Extraction from Text

We propose Marve, a system for extracting measurement values, units, and related words from natural language text. Marve uses conditional random fields (CRF) to identify measurement values and units, followed by a rule-based system to find related entities, descriptors and modifiers within a sentence. Sentence tokens are represented by an undirected graphical model, and rules are based on part-of-speech and word dependency patterns connecting values and units to contextual words. Marve is unique in its focus on measurement context and early experimentation demonstrates Marve’s ability to generate high-precision extractions with strong recall. We also discuss Marve’s role in justifying NASA JPL’s proposed HyspIRI mission, a hyper spectral infrared imaging satellite that will study the world’s ecosystems. In general, our work with HyspIRI demonstrates the value of semantic measurement extractions in characterizing quantitative discussion contained in large corpuses of natural language text. These extractions accelerate broad-cross cu ing literature surveys and expose researchers and scientists new algorithmic approaches and experimental nuances. They also facilitate identification of scientific opportunities enabled by HyspIRI leading to more informed scientific investment and research.

Mattmann, Chris A.↗
NASA NTRS2010

Models Extracted from Text for System-Software Safety Analyses

This presentation describes extraction and integration of requirements information and safety information in visualizations to support early review of completeness, correctness, and consistency of lengthy and diverse system safety analyses. Software tools have been developed and extended to perform the following tasks: 1) extract model parts and safety information from text in interface requirements documents, failure modes and effects analyses and hazard reports; 2) map and integrate the information to develop system architecture models and visualizations for safety analysts; and 3) provide model output to support virtual system integration testing. This presentation illustrates the methods and products with a rocket motor initiation case.

Malin, Jane T.↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Library-AI-Toolset

Collection of tools designed to parse documents, such as PDFs, and extract structured elements including URLs, citation contexts, tables, formulas, and figures. This toolset leverages AI-based text extraction and classification methods, providing robust solutions for various scholarly resources processing needs.

Balakireva, Lyudmila↗
NASA NTRS1993

NASA's online machine aided indexing system

This report describes the NASA Lexical Dictionary, a machine aided indexing system used online at the National Aeronautics and Space Administration's Center for Aerospace Information (CASI). This system is comprised of a text processor that is based on the computational, non-syntactic analysis of input text, and an extensive 'knowledge base' that serves to recognize and translate text-extracted concepts. The structure and function of the various NLD system components are described in detail. Methods used for the development of the knowledge base are discussed. Particular attention is given to a statistically-based text analysis program that provides the knowledge base developer with a list of concept-specific phrases extracted from large textual corpora. Production and quality benefits resulting from the integration of machine aided indexing at CASI are discussed along with a number of secondary applications of NLD-derived systems including on-line spell checking and machine aided lexicography.

Silvester, June P.↗
NASA NTRS2021

A Fully Automated Approach to Requirement Extraction from Design Documents

Design documents are intended to outline the goalsof a system or project, which are utilized in the creation ofspecific software requirements. At the NASA Jet PropulsionLaboratory, California Institute of Technology, Functional DesignDescription (FDD) documents describe the scope of theproject and reflect the design and implementation of the system.The specifications in the document are not explicitly writtenas requirements, though these guidelines must be reflected inthe official software requirements. In this work we present afully automatic approach to extracting software requirementsfrom design documents as well as comparing the extractedrequirements to those that exist in the official software requirementdatabase. We do this through (1) sentence extractionfrom the design document, (2) the incorporation of coreferenttext, and (3) aligning the extracted text to the official softwarerequirements. Via natural language processing and informationretrieval techniques, our system results in an automated processthat ensures that the specifications in the design document resultin official software requirements. We find that extraction ofimperatives results in a recall rate of 0.73 and the TF-IDF cosinesimilarity metric is shown to be a useful and successful way tocompare requirements.Though there has been recent work investigating the usefulnessof natural language processing techniques in requirement engineering,this has not been made use of in the aerospace industry.Aerospace requirement engineering is a field particularly ripefor this type of innovation because these techniques can bothautomate some of needlessly manual work and contribute toaerospace safety practices by identifying issues that a humanmay miss. We present the first fully automated approach thatextracts requirements from a design document and comparesthem to a database, and use these findings as encouragementfor future work that makes use of natural language processingtechniques in aerospace requirement engineering.

Briggs, Paul↗
DOE OSTI2023

Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications

We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases

A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.

BERT↗
NASA NTRS1994

Machine-aided indexing at NASA

This report describes the NASA Lexical Dictionary (NLD), a machine-aided indexing system used online at the National Aeronautics and Space Administration's Center for AeroSpace Information (CASI). This system automatically suggests a set of candidate terms from NASA's controlled vocabulary for any designated natural language text input. The system is comprised of a text processor that is based on the computational, nonsyntactic analysis of input text and an extensive knowledge base that serves to recognize and translate text-extracted concepts. The functions of the various NLD system components are described in detail, and production and quality benefits resulting from the implementation of machine-aided indexing at CASI are discussed.

Silvester, June P.↗
NASA NTRS2018

Using a Knowledge Graph to Discover Earth Science Information

Knowledge graphs link key entities within a specific domain to other entities via relationships. Researchers are able to mine these relationships from numerous sources to infer new knowledge. Text extraction from peer-reviewed papers and scientific reports are untapped resources that can be leveraged by knowledge graphs to accelerate scientific discovery.

Freitag, Brian↗
NASA NTRSDate not supplied

Exploring Semantic Search Capability of Graph Convolutions Over a Knowledge Graph Built Using Earth Science Corpora

Traditional knowledge graphs tend to be too generic, and often perform poorly on complex scientific queries. Often times, precedence is given to pop culture over scientific knowledge for queries. This is predominantly due to the use of internet sources for building the knowledge graph. With this work, we aim to explore the effectiveness of combining a knowledge graph generated from earth science corpora with a language model and graph convolutions for the purpose of surfacing latent and related sentences given a natural language query. In this model, sentences are conceptualized in the graph as nodes which are connected through entities—words and phrases of interest found in the text—extracted using Google Cloud’s entity extraction model. The language model we used for this is Bidirectional Encoder Representations from Transformers (BERT).The sentences are given a numeric representation by the BERT model. Graph convolutions are then applied to sentence embeddings in order to obtain a vector representation of the sentence as well as the surrounding graph structure, thereby leveraging the power of adjacency inherently encoded in graph structures. With this presentation, we demonstrate the ability of graph convolutions and their improved ability to surface relevant, latent information based on the subject of the input query.

Muthukumaran Ramasubramanian↗
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗