The Diffbot Knowledge Graph

Presented by Mike Tung, CEO of Diffbot, an adviser at the Stanford StartX accelerator, and the leader of Stanford’s entry in the DARPA Robotics Challenge.

Leveraging structured knowledge, whether about your organization or the outside world, will be a critical ingredient in the design of the next wave of intelligent applications for everything from new app experiences to search assistants to enterprise business intelligence. Mike Tung explains how Diffbot has built the world's largest knowledge graph, using an approach of developing technology for automatically reading and understanding information from the web.

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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.

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