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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
In Defense of Inconsistency, On Managing Truth in a Knowledge Graph
It is well known that work in AI fails to implement diverse viewpoints and generalize requirements adequately. Results are often unreliable, inaccurate and biased. The scope of this issue has only been magnified as LLMs have gained prominence and we need to be concerned about the trajectory of how AI systems are influencing our perceptions of truth and with the social conditioning and technical processes by which AI systems instantiate truth. As systems designed for integration of heterogeneous sources into a homogeneous system, this challenge is particularly pressing for knowledge graphs. Knowledge graphs are often presented as an AI solution for increasing the understandability of complex data landscapes, facilitating reliable pipelines for sharing or integrating data in order to gain new insights. However, the commonly deployed architecture is characterized by creating a single standardized ontology with the lens of establishing a single source of truth for all data needs. Using a number of real examples, we demonstrate that this imposes a counterproductive inflexibility rendering our graphs less useful. Contrary to this approach, and based on science from fields such as cognitive science, sociology and linguistics, we argue for a perspective that allows for more flexibility in the integration of datasets. Our knowledge graph design needs to allow for a greater plurality of vocabularies and ontologies and even inconsistency while still allowing for the data integration objectives originally conceived for knowledge graphs. We present an architecture utilizing extant RDF/OWL standards and an approach that supports a more pluralistic, efficient and effective knowledge graph development.