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.
View Christophe’s slides from the GraphTour Meetup that took place March 1, 2018.
Knowledge Graphs are becoming the de-facto solution for managing complex aggregated knowledge, and Neo4j is the leading platform for storing and querying connected data. In this talk, Christophe will describe a graph-centric cognitive computing pipeline and detail the process from the ingestion of unstructured text up to the generation of a knowledge graph, queryable using natural language through chatbots built with IBM Watson Conversation.
GraphAware is pleased to announce the release of “Neo4j : Déploiement”, a french book explaining how to use Neo4j in a real life project. The book is co-authored by Sylvain Roussy and Nicolas Rouyer along with our Senior Consultant Nicolas Mervaillie. You can get it from your favorite (french) bookstore or on D-Booker website.
See how combining technologies adds another level of quality to search results. In this new Refcard, we include code and examples for using Elasticsearch to enable full-text search and Neo4j to power graph-aided search.
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.
Graph Databases are naturally well-suited for building recommendation engines. In this talk, Christophe will share his experience building a number of production-ready recommendation engines using Neo4j and introduce the open-source GraphAware Reco4PHP Library, which enables PHP developers to rapidly build their own recommendation systems.
