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== Agenda ==
 
== Agenda ==
* Presentation by '''Yolanda Gil''' ''Computational Knowledge Graphs for Scientific Provenance and Reproducibility''
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* Presentation by '''Yolanda Gil''' ''Seven Ontologies for Publishing the Scientific Record on the Web''
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* Abstract:
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This talk will describe our work on seven ontologies that we have developed to describe complementary aspects of scientific work, and that interlinked together present a path towards publishing the scientific record on the Web.  The Linked Earth Ontology extends existing standards, and was developed collaboratively and entirely online by scientists.  The OntoSoft ontology describes scientific software artifacts with information relevant to scientists.  The W3C PROV-O ontology represents provenance of scientific data, whether observable or derived through computation. The P-PLAN ontology extends PROV-O to describe high-level general plans, and the OPMW-PROV ontology extends both to describe abstract computational workflows linked to their executions.  The DISK Hypothesis ontology describes hypothesis statements, their supporting evidence, and their revisions as new data is analyzed.  The Software Description Ontology for Models characterizes the development of models so they can be understood and compared.  These seven ontologies provide essential capabilities, but much work remains to be done to capture more comprehensively the scientific record.  Are we far from a day when each scientific article will be properly linked to hypotheses, models, software, provenance, workflows, and other key scientific entities on the Web?  Will AI research tools then be able to access this information to generate new results?  Will AI systems ultimately be capable of writing the scientific papers in the future?
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http://ontologforum.org/YolandaGil.jpg
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* Bio:
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Dr. Yolanda Gil is Director of Knowledge Technologies at the Information Sciences Institute of the University of Southern California, and Research Professor in Computer Science and in Spatial Sciences. She is also Associate Director for Data Science, and Director of the USC Center for Knowledge-Powered Interdisciplinary Data Science. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence.  Her research is on intelligent interfaces for knowledge capture and discovery, which she investigates in a variety of projects concerning scientific discovery, knowledge-based planning and problem solving, information analysis and assessment of trust, semantic annotation and metadata, and community-wide development of knowledge bases. Dr. Gil collaborates with scientists in many domains on semantic workflows and metadata capture, social knowledge collection, computer-mediated collaboration, and automated discovery.  She is a Fellow of the Association for Computing Machinery (ACM), and Past Chair of its Special Interest Group in Artificial Intelligence. She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and was elected as its 24th President in 2016.
 
* Discussion
 
* Discussion
  

Revision as of 16:37, 21 May 2020

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    (1)
Session Yolanda Gil
Duration 1 hour
Date/Time 27 May 2020 16:00 GMT
9:00am PDT/12:00pm EDT
5:00pm BST/6:00pm CEST
Convener KenBaclawski
Track Use Cases

Contents

Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.     (2A)

Agenda     (2B)

  • Presentation by Yolanda Gil Seven Ontologies for Publishing the Scientific Record on the Web     (2B1)
  • Abstract:     (2B2)

This talk will describe our work on seven ontologies that we have developed to describe complementary aspects of scientific work, and that interlinked together present a path towards publishing the scientific record on the Web. The Linked Earth Ontology extends existing standards, and was developed collaboratively and entirely online by scientists. The OntoSoft ontology describes scientific software artifacts with information relevant to scientists. The W3C PROV-O ontology represents provenance of scientific data, whether observable or derived through computation. The P-PLAN ontology extends PROV-O to describe high-level general plans, and the OPMW-PROV ontology extends both to describe abstract computational workflows linked to their executions. The DISK Hypothesis ontology describes hypothesis statements, their supporting evidence, and their revisions as new data is analyzed. The Software Description Ontology for Models characterizes the development of models so they can be understood and compared. These seven ontologies provide essential capabilities, but much work remains to be done to capture more comprehensively the scientific record. Are we far from a day when each scientific article will be properly linked to hypotheses, models, software, provenance, workflows, and other key scientific entities on the Web? Will AI research tools then be able to access this information to generate new results? Will AI systems ultimately be capable of writing the scientific papers in the future?     (2B3)

Dr. Yolanda Gil is Director of Knowledge Technologies at the Information Sciences Institute of the University of Southern California, and Research Professor in Computer Science and in Spatial Sciences. She is also Associate Director for Data Science, and Director of the USC Center for Knowledge-Powered Interdisciplinary Data Science. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence. Her research is on intelligent interfaces for knowledge capture and discovery, which she investigates in a variety of projects concerning scientific discovery, knowledge-based planning and problem solving, information analysis and assessment of trust, semantic annotation and metadata, and community-wide development of knowledge bases. Dr. Gil collaborates with scientists in many domains on semantic workflows and metadata capture, social knowledge collection, computer-mediated collaboration, and automated discovery. She is a Fellow of the Association for Computing Machinery (ACM), and Past Chair of its Special Interest Group in Artificial Intelligence. She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and was elected as its 24th President in 2016.     (2B6)

Conference Call Information     (2C)

Attendees     (2D)

Discussion     (2E)

Resources     (2F)

Previous Meetings     (2G)


Next Meetings     (2H)