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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: Industry diversity Professional Diversity: Industry practitioners, Business Users, Faculty, Scientists, Students Gender & Age diversity Geographical 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.

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Using Knowledge Graphs for Navigating Data Assets

24m

Large enterprises maintain a multitude of data assets pertaining to their businesses. It’s arduous for engineers and data scientists to not only find the information they need, but also to ensure it’s accurate and up to date. This can lead up to data duplication, misuse of assets, and conflicting results. We address this problem by leveraging an enterprise ontology, which we assume users in a domain will intuitively understand. Our approach is novel in that we allow users to navigate over their data assets semantically, using the concepts and relationships of this ontology. We present a case study involving a client that uses an ontology comprising hundreds of concepts to efficiently search for and manage a set of data assets that number in the tens of thousands. This solution uses the RelationalAI Knowledge Graph Management System to power this search process. We include a live demonstration of the working solution and discuss some important lessons learned.