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.
Chatbots and Voice Conversational Interfaces with Amazon Alexa, Neo4j and GraphAware NLP
During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database.
You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.
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.
Power of Polyglot Search
Presentation at Big Data Universe 2.0 in Budapest
Mining and Searching text with Graph Databases
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. It uses computer science, artificial intelligence and formal linguistics concepts to analyze natural language, aiming at deriving meaningful and useful information from text.