Resources - videos - page 5

Videos, Slides, Case Studies and other GraphAware related resources

Challenges in knowledge graph visualization

10 Oct 2019 videos KG visualisation

Visualizing a complex graph is a task of graph simplification and providing well-thought visual cues, the best UI goes unnoticed. This talk will summarize current approaches and present a novel user interaction pattern, which takes advantage of a performant Neo4j graph engine.

Christophe on stage with Amazon Alexa

Voice-Driven Interfaces with Neo4j and Amazon Alexa

09 May 2018 videos Neo4j

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 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

28 Mar 2018 videos ML

Graph-based machine learning is a trend in Artificial Intelligence that is gaining popularity due to its many advantages. When using graphs as a basic representation of data for ML purposes, you can benefit from the data being explicitly modeled for further analysis, representing connections and relationships between things and concepts. Additionally, graphs can easily combine multiple sources into a single graph representation and learn from them, creating powerful knowledge graphs. These benefits can lead to improved computation performance and higher quality results. During this talk, you’ll learn about the many benefits of using graph-based machine learning and how it can be applied in the context of recommendation engines and natural language processing. Don’t miss out on this opportunity to learn more about this exciting trend and how it can help you unlock the full potential of your data.