Wikidata, Knowledge Graphs, and Beyond

Presented by Denny Vrandecic, founder of Wikidata.
sps.columbia.edu/executive-edu...
Wikidata has - in a short time - become the most active collaborative knowledge base in the world. It is used by Wikipedia, Apple, Google, Library of Congress, and many other organizations. We present Wikidata and how it can be used to support your own Knowledge Graph. Knowledge Graphs have developed to important resources to represent knowledge within an organization. Ontologies formalize the shared understanding of the data within the Knowledge Graph. But the most expressive and widespread languages that we know of are human natural languages, and the largest knowledge base we have is the wealth of text written in human languages. Is there a path to bridge the gap between knowledge representation languages such as OWL and human natural languages such as English?
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Offered on Columbia University’s Morningside campus in New York City, the Knowledge Graph Conference (KGC) is a world-class curated program that brings experienced practitioners, technology leaders, cutting-edge researchers, academics and vendors together for two days of presentations, discussions and networking on the topic of knowledge graphs.
While the underlying technologies to store, retrieve, publish and model knowledge graphs have been around for a while, it is only in recent years that widespread adoption has started to take hold.
As knowledge is an essential component of intelligence, knowledge graphs are an essential component of AI. They form an organized and curated set of facts that provide support for models to understand the world. Today, they power tasks like natural language understanding, search and recommendation, and logical reasoning. Tomorrow they will ubiquitously be used to store and retrieve facts learned by intelligent agents.
In the enterprise, knowledge graphs are the ultimate dataset. Integrating and organizing together internal and external data sources. Knowledge graphs integrate with the larger information system: master data management, data governance, data quality. Their flexibility and powerful representation capabilities allow data scientists to tap them to build powerful models.
The Knowledge Graph Conference is coordinated by Columbia University School of Professional Studies' Executive Education program. Visit: sps.columbia.edu/executive-edu... for more information.