Knowledge Graphs for Data Science and Machine Learning

5^th July 2022, 13:00
Dr Ernesto Jimenez Ruiz
City University of London

Abstract

The application of knowledge graphs (KG) is going beyond the original vision of the Semantic Web and KGs are starting to play a key role to organise the enterprise, GLAM, and governmental data, and they are already the backbone in several biomedical applications. Enterprises are also leveraging knowledge graphs to drive their products and make them more “intelligent”. The next steps in AI involve the creation of richer AI systems, i.e., semantically sound, explainable, and reliable. Hybrid learning and reasoning systems combining subsymbolic and symbolic representations are gaining renewed attention, within both the Machine Learning (ML) and Knowledge Representation communities, to lead to the design and creation of such richer AI systems.

Biography

Ernesto Jimenez Ruiz is a Lecturer in Artificial Intelligence at City, University of London affiliated to the Research Centers for Machine Learning and Artificial Intelligence. He is also a researcher in the Centre for Scalable Data Access (SIRIUS) at the University of Oslo, Norway. He previously held a Senior Research Associate position at The Alan Turing Institute in London (UK) and a Research Assistant position at the University of Oxford. His home university (Universitat Jaume I, Castellon, Spain) awarded a “Premio extraordinario de doctorado” (roughly translated as a Extraordinary Doctoral Award) to his doctoral thesis (Engineering category 2010-2011). His research has covered several areas, including bio-medical information processing and integration, ontology reuse, ontology versioning and evolution, ontology alignment. His current research interests focus on the application of Semantic Technology to Data Science workflows and the combination of Knowledge Representation and Machine Learning techniques. His complete list of publications can be found here. The PDF of most of the articles are available online.