Building an Enterprise Knowledge Graph at Uber: Lessons from Reality

Presented by Joshua Shinavier Ph.D., Research Scientist at Uber.

http://sps.columbia.edu/executive-education/knowledge-graph-conference/faculty/joshua-shinavier-uber

The origins of graph databases, like the origins of digital knowledge representation and inference, can be traced as far back as the 1960s. So why do “knowledge graphs” seem like a new thing? While there is a compelling vision that has powered decades of research as well as the development of robust standards like RDF and SPARQL, it is only fairly recently that large technology companies have taken the first tentative steps toward making knowledge graphs core to their business. This entails an intricate process of adapting high-level design goals to the realities of existing data infrastructure, available tools, and developer culture. Controlled vocabulary, well-understood data models and query languages, and some form of rules or reasoning are all essential, but there is frequently a mismatch between research and standards on the one hand, and practical constraints on the other. In this talk, we will present an overview of Uber’s knowledge graph and use cases, together with a discussion of the demands of very large and rapidly changing datasets, the data modeling practices that work best in our environment, and the need to uphold user data rights.

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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: http://sps.columbia.edu/executive-education for more information.

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SPS advances knowledge with purpose to move careers, communities, and markets forward. Our mission is to provide a rigorous education, informed by rapidly evolving global market needs, that supports the academic and professional aspirations of our student community. Our vision is to become the premier destination for professional education by generating interdisciplinary thought leadership, developing innovative pedagogy, and advancing globally competitive academic solutions for ambitious agents of change and impact. Through specialized programs taught by leading educators and leading-edge practitioners, SPS students gain the skills and support they need to move their careers, communities and industries forward.

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