With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
Watch a talk by Eric Wespi from Boston Scientific and GraphAware’s Eric Spiegelberg given at GraphConnect NY 2018.
Dr. Alessandro Negro, Chief Scientist at GraphAware, presents on knowledge graphs at GraphTour DC.
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.
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 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-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.
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.
SDN is a Spring Data project for Neo4j. It uses Neo4j-OGM under the hood (very much like Spring Data JPA uses JPA) and provides functionality known from the Spring Data world like repositories, derived finders or auditing. Neo4j recently released Spring Data 2.0 (Kay) / Spring Data Neo4j 5.0 and in this session we’ll show some of the new cool features. This release contains support for dynamic properties, schema based loading, field access only, and more.
In this talk, Luanne will share insights about the business value of knowledge graphs and their contribution to relevant search in an e-commerce domain for a Neo4j customer. With text search and catalog navigation being the entry point of users to the system and in fact, driving revenue, the talk will explain the challenges of relevant search and how graph models address them. Dr. Alessandro will then talk about various techniques used for information extraction and graph modelling. He will also demonstrate how to seamlessly introduce knowledge graphs into an existing infrastructure and integrate with other tools such as ElasticSearch, Kafka, Apache Spark, OpenNLP and Stanford NLP.
Speaker: Dr. Alessandro Negro, Chief Scientist at GraphAware. September 20th 2017 at Westin Hotel, Abu Dhabi UAE.
