Amy E. Hodler is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behaviour. [She’s also the co-author of the O’Reilly book, “Graph Algorithms – Practical examples in Apache Spark and Neo4j.] Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory. She tweets @amyhodler.
A Real-World Guide to Building Your Knowledge Graphs
Knowledge graphs are driving industry disruption and business transformation by bringing together previously disparate data, using connections for superior decision support, and adding context for more intelligent applications (including AI). In this session, well walk through the fundamental elements of knowledge graphs including contextual relevancy, dynamic self-updating, understandability with intelligent metadata, and the combination of heterogeneous data.
Our use cases will cover the 3 main types of knowledge graphs (context-rich search, external insights sensing, and enterprise NLP) that build on each other. You’ll hear about real-world examples that include organizations such as Refinitiv a leading provider of financial information, the German Center for Diabetes Research, eBay, and NASA.
We’ll also cover tips for getting started from data modeling and ingestion to auto-labeling and data lineage. Attend this session to see real-world knowledge graphs and walk away with practical approaches for building your knowledge graph.