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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.
Live stream preview
Neda Abolhassani & Teresa Tung | Accelerating Industry Data Integration
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 domain like so many others to which AI is applied. It involves making decisions repeatedly informed by past experience.
Our work aims to leverage our own experience, to capture and apply industry and domain experience to accelerate industry data integration. To tackle this problem we’re taking an approach that layers a Knowledge Graph on top of the Data Mesh to capture domain knowledge and to connect the different data domains.
Like many others, we’re excited about Zhamak Dehghani’s new Data Mesh architecture paradigm. It’s distributed architecture follows many of the data integration strategies from the web, and includes mature rules around data products, quality, and governance.
This talk illustrates how some machine learning techniques can help in data profiling and mapping of siloed data sources for Enterprise Knowledge Graph construction. We present how this Knowledge Graph can be employed in a Data Mesh architecture for accelerating industry data integration for an Oil and Gas use case.