Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
Dr. Miro Marchi and Michal Trnka explore 10 of the most useful graph entity states using Cypher to enrich entities with contextual information enabling powerful interactions.
Christophe Willemsen describes Role Based Access Control (RBAC), intra-cluster encryption and logging in Neo4j.
Luanne Misquitta shows how to use the results of a UNION Cypher query.
Luanne Misquitta explains how to produce good starting-point recommendations for whisky using Cypher that are of higher quality than those we see at our favourite online stores.
Vlasta Kůs takes us through converting a corpus of research papers through Natural Language Processing, entity (relation) extraction and graph algorithms to highly informative connected insights organized in a knowledge graph.
This presentation by Christophe Willemsen, CTO, GraphAware, guides you through security best practices for Neo4j development.
Christophe Willemsen, CTO, GraphAware, explains how to apply NLP to extract entities and key phrases to build and search knowledge graphs
In this video, Michal Bachman, CEO of GraphAware, discusses how graph data can be used to identify unknown unknowns in intelligence. He emphasizes the importance of context in gaining a deeper understanding of complex systems and how graph data can reveal patterns, relationships, and connections that were previously unknown. Overall, the video provides valuable insights into the potential applications of graph data in intelligence.
Mayank Gupta, SVP of Data and Wren Chan, VP of Foundational Architecture and Innovation from LPL Financial present how they use GraphAware Hume and Neo4j to power financial chat bots.
Using natural language processing, GraphAware's Hume software will extract words and phrases from COVID-19 data streams.