Resources

Videos, Slides, Case Studies and other GraphAware related resources

Knowledge Graphs and Chatbots with Neo4j and Amazon Alexa

28 Mar 2018 videos KG Neo4j NLP chatbots

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.

Graph-Powered Machine Learning - Slides

28 Mar 2018 slides ML graphs

Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Graph-Powered Machine Learning

Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Graph-Powered Machine Learning

28 Mar 2018 videos ML

Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Neo4j : Déploiement - how to use Neo4j in a real life project

17 Dec 2017 publications Neo4j

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.

Buy the book

Voice-driven Knowledge Graph Journey with Neo4j and Amazon Alexa

21 Nov 2017 slides KG Neo4j

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.