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.
Spotting Signals in Text via Natural Language Understanding
Signals are emerging pieces of information relevant to a given context and offer potential for strategic advantage in a multitude of domains. However, sorting the signal from noise on large textual data is a very tedious process for humans. We introduce a scalable approach that extracts signals f...
DRUGS4COVID: KG about drugs used in the clinical control of the coronavirus
The main objective of Drugs4Covid is to create resources, following the principles of Open Science, that facilitate the extraction of knowledge from scientific literature related to the Coronavirus. These resources can be used by scientific communities that carry out research in relation to SARS-...
Knowledge Graph Treatments for Hallucinating Large Language Models
Despite the excitement about Large Language Models (LLM), these models suffer from hallucinations problems, e.g., generating factually incorrect text. These problems restrict the development of production-ready applications. This talk will highlight the importance of combining Knowledge Graphs wi...
Unleash the value of unstructured data: NLP Applications in HCLS
Significant portions of the data generated in enterprises are unstructured and text-based. This can span the entire product lifecycle, from early research to post-launch analysis. A major challenge for companies is managing these vast amounts of text data and extracting hidden and valuable inform...
Leave no Thought Behind: Encoding Context-rich KGs from Natural Language
Many industries store vast amounts of information as natural language. Current methods for composing this text into knowledge graphs parse a small set of relations from within a larger document. The author's specific diction is approximated by the vocabulary of the model. In domains where precise...
Methods for Natural Language Search over a Knowledge Graph
Natural language search over a knowledge graph presents unique challenges as the entities of a knowledge graph differ in structure compared to traditional documents. In this talk, we discuss methods of implementing natural language search over entity space within a knowledge graph using such tech...