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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
A Knowledge Graph of Controversial Claims and its Applications
Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (e.g., events, entities). These data constitute a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. While knowledge graphs of today enable data reuse and federation thus improving information retrieval and facilitating research and knowledge discovery in various fields, they do not store informatotion about claims and related online discourse data, making it difficult to access, query and reuse this wealth of information. In my talk, I will present recent work in collaboration with the Leibniz Institute of Social Sciences GESIS (Germany), on the construction of ClaimsKG - a knowledge graph of fact-checked controversial claims, which facilitates structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata and provides ground truth data for a number of tasks relevant to the analysis of societal debates on the web. I will discuss perspectives on modelling claims in a generalized and contextualized manner, as well as related challenges such as claim disambiguation and the assessment of claim relatedness. I will present preliminary results on learning claim vector representations (embeddings) from ClaimsKG and their application for the task of automatic fact-checking.