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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis

In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here we build on J{\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.

belief↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗
DOE OSTI2018

Standard Lexicons, Coding Systems and Ontologies for Interoperability and Semantic Computation in Imaging

Standard clinical terms, codes, and ontologies promote clarity and interoperability. Within radiology, there is a variety of relevant content resources, tools and technologies. These provide the basis for fundamental imaging workflows such as reporting and billing, and also facilitate a range of applications in quality improvement and research. This article reviews the key characteristics of lexicons, coding systems, and ontologies. A number of standards are described, including International Classification of Diseases-10-Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and RadLex. Tools for accessing this material are reviewed, such as the National Center for Biomedical Ontology BioPortal system. Web services are discussed as a mechanism for semantic application development. Several example systems, workflows, and research applications using semantic technology are also surveyed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Multi-scale structural analysis of proteins by deep semantic segmentation

Abstract Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.

Eguchi, Raphael R.↗
DOE OSTI2014

Semantic search integration to climate data

In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data. DOI: 10.1109/CTS.2014.6867639

Devarakonda, Ranjeet↗
DOE OSTI2014

Scaling Semantic Graph Databases in Size and Performance

In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grained data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.

Aggregation↗
DOE OSTI2017

Visual Exploration of Semantic Relationships in Neural Word Embeddings

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Evolvix BEST Names for semantic reproducibility across code2brain interfaces

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models

Deep learning models have been shown to be vulnerable to adversarial attacks, in which perturbations to their inputs cause the model to produce incorrect predictions. As opposed to adversarial attacks in computer vision, where small changes introduced to pixel values can drastically alter a model's output while remaining imperceptible to humans, text-based attacks are difficult to conceal due to the discrete nature of tokens. Consequently, unconstrained gradient-based attacks often produce adversarial examples that lack semantic meaning, rendering them detectable through visual inspection or perplexity filters. In contrast to methods that rely on gradient-based optimization in the embedding space, we propose an approach that leverages a Large Language Model's ability to generate grammatically correct and semantically meaningful text to craft adversarial patches that seamlessly blend in with the original input text. These patches can be used to alter the behavior of a target model, such as a text classifier. Since our approach does not rely on gradient backpropagation, it only requires access to the target model's confidence scores, making it a grey-box attack. We demonstrate the feasibility of our approach using open-source LLMs, including Intel's Neural Chat, Llama2, and Mistral-Instruct, to generate adversarial patches capable of altering the predictions of a distilBERT model fine-tuned on the IMDB reviews dataset for sentiment classification.

Roa Carvajal, Maria↗
DOE OSTI2026

BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration

While standardized ontologies like the Brick schema address data heterogeneity in Building Automation Systems (BAS), accessing this semantic data remains a challenge as domain experts often lack the expertise to formulate complex SPARQL queries. To bridge this gap, we present BrickQA, a Large Language Model (LLM)-based framework that translates natural language into executable SPARQL queries through structured query decomposition, dynamic schema exploration, and inline validation. BrickQA utilizes an iterative reasoning agent to actively navigate graph topology through dynamic exploration actions without requiring exhaustive context injection or model fine-tuning. This approach effectively mitigates hallucinations, particularly in large-scale building knowledge graphs. Empirical evaluation on BuildingQA, a standardized benchmark, demonstrates that BrickQA significantly outperforms ReAct baselines, delivering a 0.291–0.355 absolute F1 improvement while achieving 3 × –12.7 × higher token cost-efficiency. Beyond these metrics, the framework maintains structural fidelity across heterogeneous buildings and remains resilient to ambiguous queries without requiring site-specific fine-tuning. Furthermore, a case study on operational analytics validates the framework’s capability to handle temporal and aggregation constraints, effectively transforming abstract semantic models into actionable facility management insights.1

Ko, Yun-Dam↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2013

Benchmark Imagery for Assessing Geospatial Semantic Content Extraction Algorithms (Final Report)

Benchmark Imagery for Assessing Geospatial Semantic Content Extraction Algorithms (the “Benchmark Imagery Project”) was a three-year endeavor funded by the Enabling Capabilities (EC) Team of DOE/NNSA’s Office of Proliferation Detection R&D, NA-22, and carried out through a collaborative effort between Los Alamos National Laboratory (LANL) and the Lawrence Livermore National Laboratory (LLNL). The primary objective was to create a set of benchmark imagery for validating algorithms designed to extract semantic content from geospatial images of industrial facilities. The goal was not to develop algorithms; rather, it was to provide information that could be used to do so. The secondary objective was to demonstrate how this information could be used for algorithm validation. This report summarizes the results of the Benchmark Imagery Project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Geospatial-Temporal Semantic Graph Evaluation for Induced Seismicity Analysis

We assess how geospatial-temporal semantic graphs and our GeoGraphy code implementation might contribute to induced seismicity analysis. We focus on evaluating strengths and weaknesses of both 1) the fundamental concept of semantic graphs and 2) our current code implementation. With extensions and research effort, code implementation limitations can be overcome. The paper also describes relevance including possible data input types, expected analytical outcomes and how it can pair with other approaches and fit into a workflow.

58 GEOSCIENCES↗
DOE OSTI2026

AI-Ready Semantic Infrastructure for CEBAF: From CED to PALS Knowledge Graphs

JLab and PNNL are jointly developing an AI-ready data ecosystem that exposes the Continuous Electron Beam Acceleration Facility’s (CEBAF’s) operational configuration, lattice description, and control-system channels to agentic optimization frameworks through a standards-based semantic layer. The effort integrates the existing facility-specific CEBAF Element Database (CED) with extensions of the emerging facility-agnostic Particle Accelerator Lattice Standard (PALS) to produce a knowledge graph (KG) containing coherent, machine-interpretable views of devices, signals, and regions. With this KG, CEBAF’s setpoints, readbacks, and device hierarchies become queryable using a uniform declarative graph query language (e.g., Neo4j Cypher), providing intents and inspectable semantics suitable for agentic control. The resulting graph-backed interfaces will allow autonomous agents to retrieve authoritative machine configurations, reason over device- and signal-level relationships, and execute tuning and diagnostic workflows without bespoke CEBAF-specific logic, thereby delivering a scalable pathway from operational data to trustworthy agentic accelerator tuning frameworks.

Zhang, He [Thomas Jefferson National Accelerator F↗
NASA NTRS1981

Formal semantic specifications as implementation blueprints for real-time programming languages

Formal definitions of language and system semantics provide highly desirable checks on the correctness of implementations of programming languages and their runtime support systems. If these definitions can give concrete guidance to the implementor, major increases in implementation accuracy and decreases in implementation effort can be achieved. It is shown that of the wide variety of available methods the Hgraph (hypergraph) definitional technique (Pratt, 1975), is best suited to serve as such an implementation blueprint. A discussion and example of the Hgraph technique is presented, as well as an overview of the growing body of implementation experience of real-time languages based on Hgraph semantic definitions.

Feyock, S.↗
NASA NTRS1993

Visualizing the semantic content of large text databases using text maps

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content.

Combs, Nathan↗
NASA NTRS1999

The Formal Semantics of PVS

A specification language is a medium for expressing what is computed rather than how it is computed. Specification languages share some features with programming languages but are also different in several important ways. For our purpose, a specification language is a logic within which the behavior of computational systems can be formalized. Although a specification can be used to simulate the behavior of such systems, we mainly use specifications to state and prove system properties with mechanical assistance. We present the formal semantics of the specification language of SRI's Prototype Verification System (PVS). This specification language is based on the simply typed lambda calculus. The novelty in PVS is that it contains very expressive language features whose static analysis (e.g., typechecking) requires the assistance of a theorem prover. The formal semantics illuminates several of the design considerations underlying PVS, the interaction between theorem proving and typechecking.

Owre, Sam↗