Learn to develop enterprise-wide knowledge graphs.
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.
We invite presenters to submit proposals through the Easychair website. Please send us an abstract, biography and additional links to projects, articles or products you would like to present.
What You Will Learn
Throughout the program, participants will learn thought leadership topics such as:
- Storing and Querying Knowledge Graphs
- Formats and Languages
- Metadata, Schemas, Ontologies, and Taxonomies
- Data Governance
- Data Quality
- Master Data Management
- Knowledge Graphs for AI
- Natural Language Processing
- Understanding Knowledge Graph Embeddings
- Search and Answer Engine Optimization
- Applications in Healthcare, Finance, Media, and Open Data