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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
Neuralsymbolic Visual Understanding and Reasoning using Deep Learning and KGs
Visual AI has made incredible progress in basic vision tasks using deep learning techniques that can detect concepts in visual scenes accurately and quickly. However, the existing techniques rely on labelled datasets that lack common sense knowledge about visual concepts and have biased distribution of visual semantic relationships. As a result, these techniques have limited visual relationship prediction performance, limiting the expressiveness and accuracy of semantic representation and downstream reasoning. We employed deep neural networks to predict visual concepts, including objects and visual relationships, and linked them to generate symbolic image representation. To alleviate the challenges above, we leveraged rich and diverse common sense knowledge in heterogenous knowledge graphs to systematically refine and enrich the generated image representation. As a result, we observed significant improvement in recall rates of visual relationship prediction (7% increase in Recall@100), expressiveness of the representation, and the performance of downstream visual reasoning tasks, including image captioning (15% increase in SPICE score) and image reconstruction. The encouraging results depict the effectiveness of the proposed approach and the impact on downstream visual reasoning.