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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
NASA NTRS2014

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Ramachandran, Rahul↗
NASA NTRS2015

A Principled Approach to the Specification of System Architectures for Space Missions

Modern space systems are increasing in complexity and scale at an unprecedented pace. Consequently, innovative methods, processes, and tools are needed to cope with the increasing complexity of architecting these systems. A key systems challenge in practice is the ability to scale processes, methods, and tools used to architect complex space systems. Traditionally, the process for specifying space system architectures has largely relied on capturing the system architecture in informal descriptions that are often embedded within loosely coupled design documents and domain expertise. Such informal descriptions often lead to misunderstandings between design teams, ambiguous specifications, difficulty in maintaining consistency as the architecture evolves throughout the system development life cycle, and costly design iterations. Therefore, traditional methods are becoming increasingly inefficient to cope with ever-increasing system complexity. We apply the principles of component-based design and platform-based design to the development of the system architecture for a practical space system to demonstrate feasibility of our approach using SysML. Our results show that we are able to apply a systematic design method to manage system complexity, thus enabling effective data management, semantic coherence and traceability across different levels of abstraction in the design chain. Just as important, our approach enables interoperability among heterogeneous tools in a concurrent engineering model based design environment.

platform-based design↗
DOE OSTI2021

Towards Semantic Search in Building Sensor Data

This paper presents a search engine system for sensor time series data and metadata in the context of building management. It takes natural language queries as input, retrieves sensor time series data, ranks them with respect to their relevance to a given query, and visualizes the time series as search results. In addition, the system allows users to interact with the search results: they can define events of interest in the visualized results and search across sensor data for similar events, i.e., the search by example scheme. Quantitative evaluations and user studies demonstrate the value of this system for managing building sensor data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2015

NASA's ATM Ontology: Semantic Integration and Querying Across NAS Data Sources

NASA is developing an Air Traffic Management (ATM) Ontology as part of an advanced prototyping activity that demonstrates the utility of semantic technologies for integrating, querying, and searching over various sources of heterogeneous ATM data. The ontology encodes an overarching data model that functions as the backbone upon which to overlay data from multiple sources published by FAA, NOAA, NASA, and others. The integrated data can be queried to produce results not achievable using any single source alone. The ontology incorporates flight data, weather data, traffic management advisory data, airport delay data, and national airspace infrastructure data for a very limited spatial and temporal slice of airspace operations (one day of operations at a major airport).

air traffic management↗
NASA NTRS2004

Digital Archive Issues from the Perspective of an Earth Science Data Producer

Contents include the following: Introduction. A Producer Perspective on Earth Science Data. Data Producers as Members of a Scientific Community. Some Unique Characteristics of Scientific Data. Spatial and Temporal Sampling for Earth (or Space) Science Data. The Influence of the Data Production System Architecture. The Spatial and Temporal Structures Underlying Earth Science Data. Earth Science Data File (or Relation) Schemas. Data Producer Configuration Management Complexities. The Topology of Earth Science Data Inventories. Some Thoughts on the User Perspective. Science Data User Communities. Spatial and Temporal Structure Needs of Different Users. User Spatial Objects. Data Search Services. Inventory Search. Parameter (Keyword) Search. Metadata Searches. Documentation Search. Secondary Index Search. Print Technology and Hypertext. Inter-Data Collection Configuration Management Issues. An Archive View. Producer Data Ingest and Production. User Data Searching and Distribution. Subsetting and Supersetting. Semantic Requirements for Data Interchange. Tentative Conclusions. An Object Oriented View of Archive Information Evolution. Scientific Data Archival Issues. A Perspective on the Future of Digital Archives for Scientific Data. References Index for this paper.

Barkstrom, Bruce R.↗
DOE OSTI2021

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

With the increasing digitalization of processes throughout the lifecycle of buildings, data exchanged between stakeholders and between building systems has grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent, hindering the development of applications that can be reused across buildings and limiting the scalability of innovative solutions. Semantics refers to the description of the meaning of the data in a way that can be consistently understood by applications. Recently, several competing initiatives have been developing metadata schemas and ontologies to express this semantic information for different applications in the building domain. This paper systematically reviews these schemas and conducts an analysis of five of them to evaluate their applicability to three high-value use cases for building operations: energy audits, automated fault detection and diagnostics and optimal control. The survey finds 40 schemas published in the last 10 years but but their actual use in industry is difficult to estimate. Among the five selected ontologies, several gaps are highlighted in relation to the three use cases. Recommendations for the future include better harmonization of these initiatives, more centralized repositories and search engines for these schemas as well as better industry engagement to facilitate their adoption.

Smart Building, Sematic, Metadata, Ontology, Data ↗
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 OSTI2022

Integrating multimodal data through interpretable heterogeneous ensembles

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

F-OWL: An Inference Engine for Semantic Web

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Zou, Youyong↗
DOE OSTI2015

Methods and apparatus for capture and storage of semantic information with sub-files in a parallel computing system

Techniques are provided for storing files in a parallel computing system using sub-files with semantically meaningful boundaries. A method is provided for storing at least one file generated by a distributed application in a parallel computing system. The file comprises one or more of a complete file and a plurality of sub-files. The method comprises the steps of obtaining a user specification of semantic information related to the file; providing the semantic information as a data structure description to a data formatting library write function; and storing the semantic information related to the file with one or more of the sub-files in one or more storage nodes of the parallel computing system. The semantic information provides a description of data in the file. The sub-files can be replicated based on semantically meaningful boundaries.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services.

Narock, Thomas↗
DOE OSTI2016

Towards Cache-Enabled, Order-Aware, Ontology-Based Stream Reasoning Framework

While streaming data have become increasingly more popular in business and research communities, semantic models and processing software for streaming data have not kept pace. Traditional semantic solutions have not addressed transient data streams. Semantic web languages (e.g., RDF, OWL) have typically addressed static data settings and linked data approaches have predominantly addressed static or growing data repositories. Streaming data settings have some fundamental differences; in particular, data are consumed on the fly and data may expire. Stream reasoning, a combination of stream processing and semantic reasoning, has emerged with the vision of providing "smart" processing of streaming data. C-SPARQL is a prominent stream reasoning system that handles semantic (RDF) data streams. Many stream reasoning systems including C-SPARQL use a sliding window and use data arrival time to evict data. For data streams that include expiration times, a simple arrival time scheme is inadequate if the window size does not match the expiration period. In this paper, we propose a cache-enabled, order-aware, ontology-based stream reasoning framework. This framework consumes RDF streams with expiration timestamps assigned by the streaming source. Our framework utilizes both arrival and expiration timestamps in its cache eviction policies. In addition, we introduce the notion of "semantic importance" which aims to address the relevance of data to the expected reasoning, thus enabling the eviction algorithms to be more context- and reasoning-aware when choosing what data to maintain for question answering. We evaluate this framework by implementing three different prototypes and utilizing five metrics. The trade-offs of deploying the proposed framework are also discussed.

Data Discovery↗
DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗