Skip to main content

This video requires you to be logged in to view it.

To purchase access, send an email with the link below.
Price: $2.99

Purchase

VIDEO
Add To List

Knowledge Graph Maintenance

Knowledge graphs are increasingly built using complex multifaceted machine learning based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. I then discuss the challenges of ongoing system maintenance, knowledge graph quality and traceability.
Topic
Academic
Date
May 6th 2020, 10:40:00 AM
Keywords
knowledge graph, machine learning, information extraction, Academic
Slides
https://zenodo.org/record/3813905

Additional Information

Paul Groth

2

University of Amsterdam

Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab.org). Paul led the design of a number of large scale data integration and knowledge graph construction efforts in the biomedical domain. Paul was co-chair of the W3C Provenance Working Group and continues to research data provenance and data integration.

Lightening talks from the Main Stage along with speaker Q&A sessions

May 4-7, 2020

Virtual

Knowledge Graphs form an organized and curated set of facts that provide support for models to help understand the world. This conference gathers technology leaders, researchers, academics, vendors — and most importantly, practitioners, who know the discipline. For KGC 2020, attendees can participate from wherever they want in the world, from the comfort of their homes. We will stream the content, provide access to our speakers and support chat and networking as well as give access to all of the content live and on-demand after the event.