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.
Presentation by Dr. Alessandro Negro, Chief Scientist at GraphAware and author of the Manning’s book Graph-powered machine learning, that covers the following topics:
Why unlimited scale is important when using graph databases
The new graph database scaling capabilities built by Neo4j developers
The role of graphs to support machine learning application
How Neo4j assists customers in scaling their applications
Concrete examples of machine learning projects that can leverage graph sharding
The recording is available as well: https://bit.ly/39ZqFVE
View Christophe’s slides from the GraphTour Meetup that took place March 1, 2018.
In 2016, 25% of web searches on Android were made by voice and this percentage is predicted to double by 2018. From Amazon Alexa to Google Home, smartwatches and in-car systems, touch is no longer the primary user interface. In this talk, Alessandro and Christophe will demonstrate how graphs and machine learning are used to create an extracted and enriched graph representation of knowledge from text corpus and other data sources. This representation will then be used to map user intents made by voice to an entry point in this Neo4j backed knowledge graph. Every user interaction will then have to be taken into account at any further steps and we will highlight why graphs are an ideal data structure for keeping an accurate representation of a user context in order to avoid what is called machine or bot amnesia. The speakers will then conclude the session by explaining about how recommendations algorithms are used to predict next steps of the user’s journey.
A few weeks ago Spring Data Neo4j version 5 was released as part of the Spring Data 2.0 release train. Time to present the Spring way to work with Neo4j and introduce the latest features SDN 5 and its supporting library Neo4j-OGM 3 provide. The talk will also give an overview of the overall architecture and shows examples how to build modern, compact back-ends and web-applications using Spring Data Neo4j. Of course we will give a glance of what the future will bring to Spring Data Neo4j.
Neo4j as a viable tool in a relevant search ecosystem demonstrating that it offers not only a suitable model for representing several complex data, like text, user models, business goal, and context information but also providing efficient ways for navigating this data in real time. Moreover at an early stage in the “search improvement process” Neo4j can help relevance engineers to identify salient features describing the content, the user or the search query, later will be helpful to find a way to instruct the search engine about those features through extraction and enrichment.
Moreover, the talk demonstrates how the graph model can provide the right support for all the components of the relevant search and concludes with the presentation of a complete end-to-end infrastructure for providing relevant search in a real use case. It will show how it is integrated with other tools like Elasticsearch, Apache Kafka, Stanford NLP, OpenNLP, Apache Spark.
Vince Bickers, Principal Consultant at GraphAware and main contributor to Spring Data Neo4j, gives an update on the release of the new version of SDN.
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.
