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Session Knowledge Graph Approach to Combat COVID-19
Duration 1 hour
Date/Time 09 Mar 2022 17:00 GMT
9:00am PST/12:00pm EST
5:00pm GMT/6:00pm CET
Convener Ravi Sharma
Track Pandemics

Contents

Ontology Summit 2022 Knowledge Graph Approach to Combat COVID-19     (2)

Pandemics and Other Disasters     (2A)

Agenda     (2B)

  • Michael DeBellis Semantic Technology and the COVID-19 Pandemic Slides     (2B1)
    • Michael DeBellis is an independent researcher and consultant. Previously he was a partner with Deloitte Consulting and their eBusiness Chief Technology Officer. He also ran the Deloitte Western Region solution center lab in San Francisco. Prior to that he was a scientist at Accenture's Center for Strategic Technology and Research (CSTaR) where he was principal investigator for Accenture's Knowledge Based Software Assistant (KBSA) program. Prior to that he was a research associate at the Information Sciences Institute (ISI) and a senior staff in Accenture's Artificial Intelligence group. He has an MS degree in Computer Science from the University of Southern California and a BS/BA degree in computer science, psychology, and philosophy from the University of IL. at Chicago. His research interests include the Semantic Web, Knowledge Graphs, Software Engineering methods, and Evolutionary Psychology.     (2B1A)
  • Biswanath Dutta CODO: an ontology for collection and analysis of multiparadigm COVID-19 data Slides     (2B2)
    • Biswanath Dutta is an Associate Professor at the Documentation Research and Training Centre (DRTC) of Computer and Communication Sciences Division of the Indian Statistical Institute (ISI), Bangalore Centre, INDIA. He holds a Bachelors's degree in Physics (1999) and a Bachelors's degree in Library and Information Science (LIS) (2000). He holds a master's degree in LIS from ISI Bangalore (2003) and has obtained a Ph.D. degree in LIS from the University of Pune (India), 2010 for his research conducted in ISI Bangalore. His area of study was online learning systems and semantic technologies. He was a Post-Doctoral Fellow at the Department of Information Engineering and Computer Science (DISI) of University of Trento, Italy (2009-2012). He has worked as a research assistant at the Department of Computer Science, Dalhousie University, Halifax, Canada. He has visited Interactive Media Lab, University of Toronto, Canada, and Max Plank Institut Informatic (MPII), Germany. He has actively worked on a European Commission funded research project ‘LivingKnowledge: LivingKnowledge - Facts, Opinions, and Bias in Time’ [1]. He has worked in ITPAR II and III. Currently, he is handling (as a PI) a research project on COVID-19 and knowledge graph funded by Indian Statistical Institute, Kolkata [2]. He provides consultancy services to the industries. He currently serves as a secretary of the International Society for Knowledge Organization (ISKO) - India Chapter. He has published more than 50 scientific papers in national and international journals and conferences. He has four books to his credit. His current research interests lie in the areas of ontology, metadata, knowledge graph, data science, and semantic technologies (for more, visit [3]/).     (2B2A)

Conference Call Information     (2C)

Attendees     (2D)

Discussion     (2E)

[12:14] RaviSharma: hello and welcome     (2E1)

[12:15] RaviSharma: Michael - What about Ramsidivir and others repurposed, did you keep track of these. Can you also follow groups where combinations of drugs are used, such as steroids interaction?     (2E2)

[12:17] RaviSharma: Michael - How do you use semantics in a query?     (2E3)

[12:22] Asiyah Lin: I'd like to say CIDO is an ontology rather than a vocabulary, maybe controlled vocabulary is more precise. And the whole OBO ontologies the same, should be more ontologies rather than vocabularies.     (2E4)

[12:23] RaviSharma: Are KGs a part of Property Graph?     (2E5)

[12:25] Asiyah Lin: Michael, your observation is correct. OWL has a higher request, it is more for a knowledge system, rather a data system.     (2E6)

[12:35] RaviSharma: For Dr Dutta - Are all types / combos of queries like the one you showed possible, for example multiple drugs used or by comorbidities and in another level which and which genes are affected?     (2E7)

[12:36] RaviSharma: For Both: How does virus genome interact with human genome? Are there any overlaps and overlays between them?     (2E8)

[12:42] RaviSharma: What distributions and other aspects of multivariate analysis are important in CODO?     (2E9)

[12:44] Gary Berg-Cross: CIDO reuses some OBO foundry work. Did CODO reuse the same work for vaccines, Dx, treatments etc.? How harmonized is CODO with CIDO?     (2E10)

[12:46] Gary Berg-Cross: What was the pattern used for weather situation?     (2E11)

[12:47] RaviSharma: How many individuals and institutes are collaborating now - active, informational and contributors and in analysis as well?     (2E12)

[12:49] Gary Berg-Cross: I now think of KG design methods as a hybrid that brings together data management methods with graph development methods that leverage ontology engineering methods. Worth a paper if anyone interested.....     (2E13)

[12:51] Gary Berg-Cross: The materials consulted slide is a rich one that would take time to understand.     (2E14)

[12:51] RaviSharma: Dr Dutta - Are you able to see gaps among different sources for example private, state and federal sources for same item in vocabulary or graph?     (2E15)

[12:55] Gary Berg-Cross: Understanding the relations you used in the work is critical. So for disease transmission/contact tracing you have "has close relationship with" between people.     (2E16)

[13:00] RaviSharma: Q for both - How can KG SQL developed by Ken Baclawski be used?     (2E17)

[13:13] Ram D. Sriram: Do you have a COVID disease ontology in your system. If so, what are its characteristics     (2E18)

[13:31] Gary Berg-Cross and Asiyah Lin: For real world data and evidence, please check out this group: OHDSI (https://www.ohdsi.org/) Observational Health Data Sciences and Informatics     (2E19)

Resources     (2F)

Previous Meetings     (2G)


Next Meetings     (2H)