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.
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing. By Alessandro Negro, Chief Scientist, GraphAware.
Bryce Merkl Sasaki of Neo4j interviews Luanne Misquitta, Senior Consultant at GraphAware, during Graph Connect Europe 2016. Luanne Misquitta talks about Spring Data Neo4j 4.x, a completely rewritten version of SDN to support a high performance object/graph map. Version 4.1 supports both an embedded library as well as Bolt, the new binary protocol for Neo4j.
Real-Time Recommendations with Graphs and the Future of Search: Michal Bachman, Managing Director, GraphAware. Michal talks about how they use Neo4j in combination with Elasticsearch to power real-time recommendations.
