Connecting the Knowledge Ecosystem Founded in 2019 at Columbia University, The Knowledge Graphs Conference is emerging as the premiere source of learning around knowledge graph technologies. We believe knowledge graphs are an underutilized yet essential force for solving complex societal challenges like climate change, democratizing access to knowledge and opportunity, and capturing business value made possible by the AI revolution.
KGC bridges the gap between industry, which is increasingly recognizing the necessity of integrated data, and academia, where semantic technologies have been developing for over twenty years. Our events, education, content, and community efforts facilitate meaningful exchange between diverse groups, and increase awareness, development and adoption of this powerful technology.
Conference – bridging the gap between research and industry
We organize workshops and tutorials to progress a number of Tech4Good themes, targeting objectives such as the United Nations Sustainable Development Goals and the development of a COVID-19 vaccine. At our most recent conference, 530 attendees participated, representing over thirty industries across forty-two countries. Speakers ranged from Bell Labs pioneer John Sowa to Morgan Stanley, AstraZeneca, and leading academics from Europe and USA. A variety of workshops and tutorials were also given, including several on tech4good themes–from the UN SDGs to personal health graphs and fake news.
KGC Vision and Values
Our goal is to build the community and become a leading source of learning around knowledge graphs.
We will achieve this by engaging and convening industry leaders and innovators, across sectors.
We will focus on the diversity of perspectives:
Professional Diversity: Industry practitioners, Business Users, Faculty, Scientists, Students
Gender & Age diversity
We will gather, share and publish content to increase learning.
We will build the community online and in-person through our content, meetups and conferences.
For over half a century organizations have assumed that data is an asset to collect more of, and data must be centralized to be useful. These assumptions have led to centralized and monolithic architectures such as data warehousing and data lake, and neither of which have been able to enable data...
A data supply chain is industry-specific, but many data prep tools are industry agnostic. As part doing this work, data engineers and domain experts apply their deep knowledge of how to transform raw data to a form that can address specific problems. In this way, the data supply chain is a doma...
Intuit is embarking on a multi-year journey to transform from a financial product-centric company into an "AI-driven Expert Platform" company. We have aligned our enterprise data architecture and strategy to the company growth strategy, with a clean end-to-end enterprise architecture that embrac...