Presented by Pierre Haren is the founder and CEO of Causality Link, a US company specialized in AI applied to Fintech.

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A key element of AI is the ability to reason on reasoning. General AI will only be achieved when this capability becomes recursively unlimited. However, this capability is not possible with processes as black boxes. The more reasoning processes rely on the data-driven manipulation of explicit meaningful data, the closer we get to the ability of meta-level reasoning. The evolution of knowledge graphs can be understood through this prism, as the reification of higher levels of symbolic reasoning across time. From Sowa’s conceptual graphs to frames, then prototypes, then object-oriented rule-based or constraint programming, modern knowledge graphs and now causal graphs, we will highlight how the reification of increasingly complex levels of reasoning into data enables us to get incrementally closer to the target of reasoning on reasoning, and how this progress increases our ability to extract knowledge from experts and merge these knowledge pieces into increasingly powerful systems.

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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: [ Ссылка ] for more information.

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