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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
Content Recommendation Systems: When Do You Need a Graph?
Recommendation systems are at the heart of many products we use today, helping us discover new music, expand our wardrobes, and navigate the massive amounts of information on the Internet to answer our search queries. In a world where efficiency and accuracy is paramount, and processing power and availability of user data varies across industries, graph-powered engines and their ability to deliver continued performance at scale provide many advantages.
How are these recommendations determined? What conditions must exist in order to build a graph-based system that produces accurate, relevant results? What technologies are at play under the hood? Through the business use case of a leading national learning management system (LMS) in the healthcare industry, this presentation will answer these questions by providing an overview of the problem statement and the solution architecture, including technical dives into NLP taxonomy enrichment, knowledge graph development, and recommender logic.