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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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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.
Language Model for Earth Science for Semantic Search
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Ontology-based semantic information technology for safeguards: Opportunities and challenges.
Abstract not provided.
Temporal Analysis and Change Detection via Geospatial-Temporal Semantic Graphs.
Abstract not provided.
Approximate Pattern Matching Under Uncertainty in Geospatial Semantic Graphs.
Abstract not provided.
Facility Search in Remote Sensing Data Using Geospatial Semantic Graphs.
Abstract not provided.
Chemically Activated Quiescent Persistent Sensors for Semantic Networks, Project STL-02-14
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Utilizing Semantic Big Data for realizing a National-scale Infrastructure Vulnerability Analysis System
Critical Infrastructure systems(CIs) such as energy, water, transportation and communication are highly interconnected and mutually dependent in complex ways. Robust modeling of CIs interconnections is crucial to identify vulnerabilities in the CIs. We present here a national-scale Infrastructure Vulnerability Analysis System (IVAS) vision leveraging Se- mantic Big Data (SBD) tools, Big Data, and Geographical Information Systems (GIS) tools. We survey existing ap- proaches on vulnerability analysis of critical infrastructures and discuss relevant systems and tools aligned with our vi- sion. Next, we present a generic system architecture and discuss challenges including: (1) Constructing and manag- ing a CI network-of-networks graph, (2) Performing analytic operations at scale, and (3) Interactive visualization of ana- lytic output to generate meaningful insights. We argue that this architecture acts as a baseline to realize a national-scale network based vulnerability analysis system.
Ontology based semantic information technology for safeguards.
Abstract not provided.
Lightweight threading with MPI using Persistent Communications Semantics.
Abstract not provided.
Lightweight Threading with MPI Using Persistent Communcations Semantics.
Abstract not provided.
SEMANTIC GRAPHS FOR PATTERN DETECTION IN SENSOR DATA FROM MONITORING REGIMES.
Abstract not provided.
Reconciling SCXML Statechart Representations and Event-B Lower Level Semantics.
Abstract not provided.