Query use cases
Query use cases
part of the Knowledge graphs theme from CEGIS
Computing graph-based queries involve multiple relations across hierarchies of data. Queries are closely associated with questions using language.
This project develops query use case approach using a grammar ontology. Queries focus on topographic information of diverse thematic types.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
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Open knowledge network roadmap: Powering the next data revolution
Open access to shared information is essential for the development and evolution of artificial intelligence (AI) and AI-powered solutions needed to address the complex challenges facing the nation and the world. The Open Knowledge Network (OKN), an interconnected network of knowledge graphs, would provide an essential public-data infrastructure for enabling an AI-driven future. It would...AuthorsChaitan Baru, Martin Halbert, Lara Campbell, Tess DeBlanc-Knowles, Jemin George, Wo Chang, Adam Pah, Douglas Maughan, Ilya Zaslavsky, Amanda Stathopoulos, Ellie Young, Kat Albrecht, Amit Sheth, Emanuel Sallinger, Katerine Osatuke, Angela Rizk-Jackson, Eric Jahn, Kenneth Berkowitz, Bandana Kar, Erica Smith, Krzystof Janowicz, Brian Handspicker, Esther Jackson, Lauren Sanders, Chengkai Li, Florence Hudson, Lilit Yeghiazarian, Cogan Shimizu, Glenn Ricart, Louiqa Raschid, Dalia E. Varanka, Greg Seaton, Luis Amaral, Oktie Hassanzadeh, Silviu Cucerzan, Matt Bishop, Ora Lassila, Sharat Israni, Matthew Lange, Pascal Hitzler, Ryan McGranaghan, Michael Cafarella, Paul Wormeli, Todd Bacastow, Sam Klein, Murat Omay, Sergio Baranzini, Ying Ding, Nariman AmmarA geospatial knowledge graph prototype for national topographic mapping
Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial...AuthorsDalia E. VarankaGeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...AuthorsE. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. VarankaMapping interactive geospatial linked data
No abstract available.AuthorsWilliam (Contractor) Baumer, Logan J. Powell, Dalia E. VarankaAn applied ontology for semantics associated with surface water land cover
No abstract available.AuthorsDalia E. Varanka, E. Lynn Usery
CEGIS science themes
Theme topics home
Knowledge graphs
Knowledge networks
Query use cases
Semantic graphs
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
All Query use cases publications
All Knowledge graphs publications
All CEGIS publications
Open knowledge network roadmap: Powering the next data revolution
A geospatial knowledge graph prototype for national topographic mapping
GeoAI in the US Geological Survey for topographic mapping
Mapping interactive geospatial linked data
An applied ontology for semantics associated with surface water land cover
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri

Samantha T Arundel, PhD
Research Director
Senior Science Advisor
Ethan Shavers, PhD
CEGIS Section Chief/ Supervisory Geographer
Jung kuan (Ernie) Liu
Physical Research Scientist
Computing graph-based queries involve multiple relations across hierarchies of data. Queries are closely associated with questions using language.
This project develops query use case approach using a grammar ontology. Queries focus on topographic information of diverse thematic types.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
-
Open knowledge network roadmap: Powering the next data revolution
Open access to shared information is essential for the development and evolution of artificial intelligence (AI) and AI-powered solutions needed to address the complex challenges facing the nation and the world. The Open Knowledge Network (OKN), an interconnected network of knowledge graphs, would provide an essential public-data infrastructure for enabling an AI-driven future. It would...AuthorsChaitan Baru, Martin Halbert, Lara Campbell, Tess DeBlanc-Knowles, Jemin George, Wo Chang, Adam Pah, Douglas Maughan, Ilya Zaslavsky, Amanda Stathopoulos, Ellie Young, Kat Albrecht, Amit Sheth, Emanuel Sallinger, Katerine Osatuke, Angela Rizk-Jackson, Eric Jahn, Kenneth Berkowitz, Bandana Kar, Erica Smith, Krzystof Janowicz, Brian Handspicker, Esther Jackson, Lauren Sanders, Chengkai Li, Florence Hudson, Lilit Yeghiazarian, Cogan Shimizu, Glenn Ricart, Louiqa Raschid, Dalia E. Varanka, Greg Seaton, Luis Amaral, Oktie Hassanzadeh, Silviu Cucerzan, Matt Bishop, Ora Lassila, Sharat Israni, Matthew Lange, Pascal Hitzler, Ryan McGranaghan, Michael Cafarella, Paul Wormeli, Todd Bacastow, Sam Klein, Murat Omay, Sergio Baranzini, Ying Ding, Nariman AmmarA geospatial knowledge graph prototype for national topographic mapping
Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial...AuthorsDalia E. VarankaGeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...AuthorsE. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. VarankaMapping interactive geospatial linked data
No abstract available.AuthorsWilliam (Contractor) Baumer, Logan J. Powell, Dalia E. VarankaAn applied ontology for semantics associated with surface water land cover
No abstract available.AuthorsDalia E. Varanka, E. Lynn Usery
CEGIS science themes
Theme topics home
Knowledge graphs
Knowledge networks
Query use cases
Semantic graphs
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
All Query use cases publications
All Knowledge graphs publications
All CEGIS publications
Open knowledge network roadmap: Powering the next data revolution
A geospatial knowledge graph prototype for national topographic mapping
GeoAI in the US Geological Survey for topographic mapping
Mapping interactive geospatial linked data
An applied ontology for semantics associated with surface water land cover
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
