IN017
Knowledge graph, machine learning, and artificial intelligence in geosciences
Session ID#: 157064
Session Description:
It is a common understanding in artificial intelligence that the integration of primary knowledge in machine learning will help lead to a better understanding of the big data analytical techniques. Taxonomies, ontologies, and knowledge graphs can improve the explainability and mitigate the black-box nature of machine learning. Recently, there were also applications of knowledge graphs together with machine learning algorithms to improve the quality of data science processes. For instance, in remote sensing image analysis, ontologies have been applied to improve the classification results. More successes are also appearing in other domains. This session invites all geoscientists working on knowledge graphs, explainable artificial intelligence, knowledge-fused machine learning, as well as applications to submit their work. We hope the session can create a venue where scientists can share their ideas and experiences and discuss future works to better integrate knowledge graph, machine learning, and other artificial intelligence techniques in geoscience data analytics.
Co-Sponsor(s):
Index Terms:
1908 Cyberinfrastructure [INFORMATICS]
1914 Data mining [INFORMATICS]
1938 Knowledge representation and knowledge bases [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Xiaogang Ma, University of Idaho, Moscow, ID, United States
Conveners: Sanaz Salati1, Anirudh Prabhu2 and Shaunna M Morrison2, (1)University of Idaho, Department of Computer Science, Moscow, United States(2)Carnegie Institution for Science, Earth and Planets Laboratory, Washington, DC, United States
Student/Early Career Convener: Anirudh Prabhu, Carnegie Institution for Science, Earth and Planets Laboratory, Washington, DC, United States
Soil salinity prediction utilizing spatial dependence information (1041657)
Yingfu Tang1,2, Zhongjing Wang3 and Teng Zhang1, (1)Tsinghua University, Beijing, China, (2)China Institute of Water Resources and Hydropower Research, Beijing, China, (3)Tsinghua University, Department of Hydraulic Engineering, Beijing, China
Infusing knowledge models in remote sensing image processing: Progress, challenges, and opportunities (1049444)
Sanaz Salati, University of Idaho, Department of Computer Science, Moscow, United States and Xiaogang Ma, University of Idaho, Department of Computer Science, Moscow, ID, United States
NASA EOSDIS progress report on integrating knowledge graphs and machine learning (1062101)
Douglas J Newman1, Valerie Dixon2, Vincent Inverso3, Iksha Gurung4, Ashish Acharya4 and Muthukumaran Ramasubramanian4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, ESDIS, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)University of Alabama in Huntsville, Huntsville, AL, United States
Construction of a Fluvial Facies Knowledge Graph (FFKG) and Its Application in Sedimentary Facies Identification (1064779)
Lei Zhang1, Mingcai Hou1, Anqing Chen1, Hanting Zhong1 and James G Ogg1,2, (1)Chengdu University of Technology, Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications, MNR & Institute of Sedimentary Geology, Chengdu, Sichuan, China, (2)International Union of Geological Sciences, Deep-time Digital Earth Research Center of Excellence (Suzhou), Suzhou, China
Using NLP Techniques to Process a Geoscience Corpus as the Foundation of Knowledge Management (1064890)
Sandra Villacorta1, Tyler Bikaun1,2 and Mark Lindsay3,4, (1)CSIRO, Mineral Resources, Perth, Western Australia, Australia, (2)The University of Western Australia, Natural & Technical Language Processing Group, Perth, Western Australia, Australia, (3)University of Western Australia, Centre of Exploration Targeting (School of Earth Sciences), Crawley, WA, Australia, (4)CSIRO Australian Resources Research Center, Mineral Resources, Perth, Western Australia, Australia
Semantics of space, time, and mineralogy in the OpenMindat data portal (1075356)
Xiaogang Ma1, Jolyon Ralph2, Anirudh Prabhu3, Shaunna M Morrison3 and Robert Hazen3, (1)University of Idaho, Moscow, ID, United States, (2)mindat.org, Surray, United Kingdom, (3)Carnegie Institution for Science, Earth and Planets Laboratory, Washington, DC, United States
Simulating A Context-aware Heterogeneous Sensor System for Monitoring Inland Waters and Ecosystems with 3D-CHESS (1076260)
Daniel Selva1, George H. Allen2, Huilin Gao3, Benjamin Jared Gorr4, Alan Aguilar5, Antoni Viros-i-Martin5, Yizhou Sun6, Ankur Mehta6, Vinay Ravindra7 and Cedric H David8, (1)Texas A&M University College Station, College Station, TX, United States, (2)Texas A&M University, Department of Geography, College Station, United States, (3)Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, United States, (4)Texas A&M University, Department of Aerospace Engineering, College Station, TX, United States, (5)Texas A&M University, College Station, United States, (6)University of California Los Angeles, Los Angeles, United States, (7)NASA Ames Research Center, Moffett Field, CA, United States, (8)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
Explainable Clustering Models for Structural Geology Measurements (1091968)
Michał Michalak, University of Silesia, Institute of Earth Sciences, Katowice, Poland; AGH University of Science and Technology, Faculty of Geology, Geophysics and Environmental Protection, Kraków, Poland, Filip Turoboś, Lodz University of Technology, Lodz, Poland and Pawel Gladki, University of Silesia, Katowice, Poland
Python Dynamic Web Application for Rock-forming Minerals (1093267)
Armita Davarpanah, Spelman College, Environmental and Health Sciences Program, Atlanta, GA, United States and Hassan A Babaie, Georgia State University, Department of Geosciences, Atlanta, GA, United States
Search Engine for the Critical Metals (1097011)
Hassan A Babaie, Georgia State University, Department of Geosciences, Atlanta, GA, United States, Armita Davarpanah, Spelman College, Environmental and Health Sciences Program, Atlanta, GA, United States, W Crawford Elliott, Georgia State University, Atlanta, GA, United States and Juan M. Banda, Georgia State University, Computer Science, Atlanta, GA, United States
Using Large Scale Language Models for Enabling NLP Abilities Within Earth Sciences (1112836)
Muthukumaran Ramasubramanian1, Iksha Gurung1, Rahul Ramachandran2, Manil Maskey3, Raghu K Ganti4, Marquita Ellis4, Davis Wertheimer4 and Bishwaranjan Bhattacharjee4, (1)University of Alabama in Huntsville, Huntsville, AL, United States, (2)NASA Marshall Space Flight Center, Huntsville, United States, (3)NASA, Huntsville, United States, (4)IBM Research USA, Yorktown Heights, United States
Analysis of Earth Science Dataset Co-Citations using Knowledge Graphs (1121532)
Thomas Norman1,2, Irina Gerasimov3, Armin Mehrabian4, Mohammad G Khayat5, Binita KC6, Jennifer C Wei7 and Long Pham4, (1)Spring, Texas, UNITED STATES, (2)University of Texas at Austin, Computer Science, Austin, United States, (3)NASA GES DISC (ADNET Systems Inc.), Greenbelt, MD, United States, (4)NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, GES DISC, Greenbelt, United States, (6)Northeastern University, School of Public Policy and Urban Affairs, Boston, MA, United States, (7)NASA GES DISC, Greenbelt, United States
Soil Texture Classification Using Deep Learning (1172722)
Anush Kumar Kumar Kasaragod, Michigan Technological University, Geological and Mining Engineering and Sciences, Houghton, MI, United States, Thomas Oommen, Michigan Technological University, Geological and Mining Engineering and Sciences, Houghton, United States, Sidike Paheding, Michigan Technological University, Department of Applied Computing, Houghton, MI, United States and Ryan Williams, Michigan Technological University, Great Lakes Research Center, Houghton, United States
Cloud Computing Tools for Developing LASSO (L1) Regularized Remote Sensing Models for Optically Complex Waters (1173430)
Anna Cardall, Brigham Young University, Provo, United States, Riley Hales, Brigham Young University, Civil & Environmental, Provo, United States, Kaylee Tanner, Brigham Young University, Provo, UT, United States, Gustavious P Williams, Brigham Young University, Civil and Construction Engineering, Provo, United States and Kel Nicolas Markert, Google, Mountain View, United States
Knowledge Graphs in Deep Search Climate-Hub (1181925)
Surya Karthik Mukkavilli1, Birgit Pfitzmann1, Anne Jones2, Thomas Brunschwiler1, Paolo Fraccaro2, Campbell Watson3, Bianca Zadrozny4, Hendrik Hamann3 and Peter W.J. Staar1, (1)IBM Research, Zurich, Switzerland, (2)IBM Research, Daresbury, United Kingdom, (3)IBM Research, Yorktown Heights, United States, (4)IBM Research, Rio de Janeiro, Brazil
Heliophysics Network Building UtilitiesA Contribution to the Heliophysics KNOWledge Network (Helio-KNOW), Natural Language Processing and Knowledge Graphs of Heliophysics research (1194667)
Omar Shalaby, New Jersey Institute of Technology, Edison, NJ, United States, Ryan Michael McGranaghan, ASTRA LLC, Boulder, CO, United States, Brian A. Thomas, NASA Headquarters, Washington, United States and M. Chantale Damas, CUNY Queensborough Community College, Bayside, NY, United States
