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Session Anirudh Prabhu
Duration 1 hour
Date/Time 26 February 2020 16:00 GMT
9:00am PDT/12:00pm EDT
5:00pm BST/6:00pm CEST
Convener Gary Berg-Cross
Track How

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)

Abstract: Knowledge graphs have been used with reasoners to make inferences about the data, based on the assertions and axioms that have been written. But a knowledge graph, which houses complex, multi-dimensional data also contains hidden patterns and trends which cannot be explored simply by using reasoners. In this talk, we present various approaches for using data extracted from knowledge graphs to gain scientific insights.     (2B2)

Bio: Anirudh Prabhu is a PhD student at Rensselaer Polytechnic Institute, working under Prof. Peter Fox. Research interests include Data Modeling, Semantic E-science, and Data Visualization. Current research work focuses on applying Data Science and Machine Learning to solve Earth and Environmental Science problems. For more information see Anirudh Prabhu Website     (2B3)

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