Resources
Knowledge Graph Search with Elasticsearch — Luanne Misquitta and Alessandro Negro, GraphAware
In this talk, Luanne will share insights about the business value of knowledge graphs and their contribution to relevant search in an e-commerce domain for a Neo4j customer. With text search and catalog navigation being the entry point of users to the system and in fact, driving revenue, the talk will explain the challenges of relevant search and how graph models address them. Dr. Alessandro will then talk about various techniques used for information extraction and graph modelling. He will also demonstrate how to seamlessly introduce knowledge graphs into an existing infrastructure and integrate with other tools such as ElasticSearch, Kafka, Apache Spark, OpenNLP and Stanford NLP.
Podcast Interview with Nicolas Mervaillie, GraphAware
Here’s another great interview with a long time Graphista that has done a lot of really interesting work in our French community, and is now having lots of graph-fun at GraphAware: Nicolas Mervaillie.
Machine Learning Powered by Graphs
Speaker: Dr. Alessandro Negro, Chief Scientist at GraphAware. September 20th 2017 at Westin Hotel, Abu Dhabi UAE.
Neo4j Online Meetup #18: Neo4j Bolt Drivers Roundtable
Streamed live on Aug 3, 2017 Following on from the Introduction to Neo4j Bolt Drivers in this session we’ll be hosting a roundtable where Neo4j driver authors will be sharing their experiences.
Intro to Neo4j by Michael Hunger
Michael Hunger introduces Neo4j to the audience of the Czech GraphDB Meetup in Prague, Czech Republic
Chatbot and Conversational Experiences with Amazon Alexa, Neo4j and GraphAware NLP
At GraphDB Meetup Czech Republic in Prague, Christophe Willemsen talks about creating a chatbot with Amazon Alexa, Neo4j and GraphAware NLP
Cypher: Write Fast and Furious
Ever struggle with writes performance in Cypher? This Lightning talk is for you! In only 15 minutes, Christophe will show you some tips and tricks for making your Cypher write transactions as fast as possible.
The power of polyglot searching
In the previous years we have got the Polyglot Persistence. This is a fancy term which means that when storing data, it is best to use multiple data storage technologies, chosen based upon the way data is being used by. If we have multiple persistence, then sometimes we need polyglot operations. One of the most popular use case in Big Data is searching. Almost all websites provide a search function to their users, to be able to find what they are looking for. Usually it is an Apache Lucene based solution, like Elasticsearch or Solr. I will show you how to enrich this kind of searching with the power of graph based searches, and implement a polyglot search functionality, where the results are based on the cooperation of a search engine and a graph based real time recommendation.