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LinkedIn Knowledge Graph Enriches Data Value

In this contributed article, Qi He, Senior Engineering Manager – Machine Learning & Data Mining, Head of Data Standardization at LinkedIn, and Bee-Chung Chen, a Principal Staff Engineer & Applied Researcher at LinkedIn, discuss three LinkedIn strategies for enriching data value from the perspective of the Knowledge Graph.

Video: Why use Tables and Graphs for Knowledge Discovery System?

In this video from the 2016 HPC User Forum in Austin, John Feo from PNNL presents: Why use Tables and Graphs for Knowledge Discovery System? “GEMS software provides a scalable solution for graph queries over increasingly large data sets. As computing tools and expertise used in conducting scientific research continue to expand, so have the enormity and diversity of the data being collected. Developed at Pacific Northwest National Laboratory, the Graph Engine for Multithreaded Systems, or GEMS, is a multilayer software system for semantic graph databases. In their work, scientists from PNNL and NVIDIA Research examined how GEMS answered queries on science metadata and compared its scaling performance against generated benchmark data sets. They showed that GEMS could answer queries over science metadata in seconds and scaled well to larger quantities of data.”

When to Use a Graph Database vs. a Triple Store?

When should you use a graph database? In the same amount of time it took you to ask one question, we enable you to ask a thousand questions, making it more likely that you’ll discover the answer that gives you a “Urika” moment – and helps you gain competitive advantage.