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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
Using a Hybrid AI Approach to Automatically Extract Recommendations
The emerging landscape of deep learning and knowledge graph technologies provides vast opportunities to use public repositories as a source of knowledge for recommendations. Often, the knowledge is represented as a knowledge graph, and a recommendation regarding an entity instance is extracted by querying it. For example, a knowledge graph representation can be used for describing cybersecurity domain entities, such as adversarial techniques and their countermeasures. We can use this graph to recommend a specific countermeasure given a system-specific detected technique. This approach raises few challenges. First, how to correlate between the instance entity and the suitable object in the knowledge graph? Second, how to extract the recommendations from the graph? We developed a hybrid AI approach that addressed these challenges by utilizing deep learning-based language models and graph traversal algorithms. In this session we will demonstrate how we automatically categorized vulnerability descriptions discovered in specific systems according to their adversarial techniques and recommended relevant countermeasures.