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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
Automated Knowledge Base Creation in Finance
The Information Management team at Morgan Stanley has built an RDF graph and a semantic knowledge base to help answer domain specific questions, formulate classification recommendations and deliver quality search to our internal users. In doing so over the past 4 years, we also helped other departments across the firm discover and embrace semantic data modeling for their own use cases. In the first part of our presentation, we would like to briefly describe the Semantic Modeling and Ontology consortium we created within the Firm before diving deeper into our knowledge base creation effort. We think that a knowledge base reflecting concepts specifically relevant to our company is extremely valuable to develop semantic technology applications. However, populating knowledge bases can be time consuming, costly and error prone, with an end product that is difficult to maintain. In the second part of this presentation, we would like to discuss a framework for automatic generation of a Simple Knowledge Organization System (SKOS) knowledge base from unstructured text. Our Natural Language Processing (NLP) engine parses the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links -- abstractions of underlying relations between concepts that capture the overall meaning of text based on a corpus of roughly 6,500 policies and procedures published at Morgan Stanley.