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.
Turning dross into gold. Knowledge graphs, with their capacity for surfacing vast hidden networks, can help detect looted art from the ownership history - or provenance - of artworks. The cultural heritage sector and art industry have explored named entity recognition with an event-based approach using CIDOC-CRM. However, Nazi-looted art poses a particular challenge, in part due to the passage of time, and in part due to unreliable data, as attempts to conceal and distort information which began in the Nazi era continue into the digital age. Missing, confusing or badly coded entities, false dates, names, events, places, the mixing of speculation and fact occur with such frequency in Nazi-looted art that it is useful to view errors, not as anomalies to be cleansed from the dataset, but as primary features to be analyzed. This presentation focuses on strategies and methods to quantify, classify, code and exploit this unreliable information in order to detect looted art and the patterns and networks which underly its commercialisation.
Business application developers put a wealth of business knowledge into their code. Too often, aside from executing, that information is left untapped for knowledge purposes. In fact, well-designed code is ripe with valuable information that can be extracted directly from the code for use in a va...
As industries grapple with the need and various approaches to implementing Digital Transformation, a shift in thinking about data technology and data culture within organizations is required to realize its full potential. Knowledge graph technology presents an emerging approach to manage and inte...