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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.
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A Meta-Graph Solution for Recommender Systems
The proposed meta-graph solution for recommender systems is a living process for semi-automatically resolving recommendations using guided queries upon a knowledge graph. In addition, this solution is explainable; it can provide comprehensible recommendations which show the reason for each result along with a statistical measure. A detailed use case for this application which is an issue tracking system for Oil & Gas companies is presented in this work. In the introduced use case, the interlinks between failure events and their associated actions are captured in a knowledge graph. To overcome data sparsity, scarcity, and non-trivial relationships, a relatively new concept called ‘meta-graphs’ are employed. Using meta-graph patterns defined on the knowledge graph schema, we can recommend actions based on the semantic relatedness of the various failure events and their corrective actions. Moreover, the recommendation results selected by the end-user and the meta-graphs evaluated by the domain experts are automatically graded during the entire lifecycle of the system for transfer learning, reliability evaluation and reproducibility.
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