Resources

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Connect, Enrich, Evolve: Convert Unstructured Data Silos to Knowledge Graphs

Dr. Alessandro Negro, Chief Scientist at GraphAware, presents on knowledge graphs at GraphTour DC.

Connect. Enrich. Evolve. Convert unstructured data silos to knowledge graphs

View the slides from Dr. Alessandro Negro’s presentation at GraphTour DC on how to convert unstructured data silos into powerful knowledge graphs.

Lean Dependency Management: Reduce Project Delivery Chaos with Graphs

In this talk, Luanne talks about ways how to use graphs in order to reduce chaos while delivering complex projects. Streamlining dependencies by promoting zero waste.

Christophe on stage with Amazon Alexa

Voice-Driven Interfaces with Neo4j and Amazon Alexa

The age of touch could soon come to an end. From smartphones and smartwatches to home devices and in-car systems, touch is no longer the primary user interface. In this talk, Christophe will guide you through the design of Voice-Driven UIs and show why Neo4j, the world’s leading graph database, is a suitable engine for storing and computing context-aware intents in order to improve the user experience.

Knowledge graphs + Chatbots with Neo4j

View Christophe’s slides from the GraphTour Meetup that took place March 1, 2018.

Knowledge Graphs and Chatbots with Neo4j and Amazon Alexa

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

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

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

View Vlasta’s slides from Paris Meetup in March 5, 2018.

Neo4j as a Key Player in Human Capital Management (HCM), GraphAware

View Luanne’s slides from GraphConnect Europe 2017.