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                                         Keynote:
                                  Open Knowledge Network

                                                   Chaitan Baru
                                            National Science Foundation
                                                 CBARU@nsf.gov




Abstract
Knowledge networks that encode information and knowledge about real-world entities and their relationships
provide a key, enabling semantic information infrastructure for next generation, artificial intelligence-based
technologies and applications. An Open Knowledge Network (OKN) effort would help create a common se-
mantic information infrastructure to boost the next generation of data-enabled machine learning and artificial
intelligence applications. Such an open network could be formed by utilizing the data and information for
an initial set of science and engineering domains, as well as other domains of interest, driven by a set of sig-
nificant, well-defined questions addressing scientific challenges and societal problems. This talk will present
results from a community workshop on this topic which was held in October 2017 at the National Library of
Medicine (http://ichs.ucsf.edu/open-knowledge-network/). Application domains discussed at the work-
shop included geosciences, biomedicine, finance, and smart manufacturing. Workshop participants were from
industry, academia and government agencies.

Bio
Chaitan Baru is Senior Advisor for Data Science in the Computer and Information Science & Engineering
Directorate at the National Science Foundation, Alexandria, VA, where he co-chairs the NSF Harnessing the Data
Revolution Big Idea working group, and has responsibility for the cross-Foundation BIGDATA research program.
He is advisor to the NSF Big Data Regional Innovation Hubs and Spokes program (BD Hubs/Spokes) and was
engaged in the development of the NSF Transdisciplinary Research in Principles of Data Science (TRIPODS)
program. He also co-chairs the Big Data Interagency Working Group—which is part of the Networking and IT
R&D program of the National Coordination Office, White House Office of Science and Technology Policy—and is
a primary co-author of the Federal Big Data R&D Strategic Plan (released May 2016). Dr. Baru is on assignment
at NSF from the San Diego Supercomputer Center (SDSC), University of California San Diego, where he is a
Distinguished Scientist and Director of the Advanced Cyberinfrastructure Development Group (acid.sdsc.edu)
and the Center for Large-scale Data Systems Research (clds.sdsc.edu).




Copyright © by the paper’s authors. Copying permitted for private and academic purposes.
In: Joint Proceedings of the First International Workshop on Professional Search (ProfS2018); the Second Workshop on Knowledge
Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR); and the International Workshop on Data Search
(DATA:SEARCH’18). Co-located with SIGIR 2018, Ann Arbor, Michigan, USA – 12 July 2018, published at http://ceur-ws.org




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