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How Boston Scientific Improves Manufacturing Quality Using Graph Analytics

Watch a talk by Eric Wespi from Boston Scientific and GraphAware’s Eric Spiegelberg given at GraphConnect NY 2018.

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

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

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 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-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.

Knowledge Graphs and Chatbots with Neo4j and IBM Watson

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.

Neo4j Online Meetup #30: Spring Data Neo4j 5 and OGM3

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.

Knowledge Graph Search with Elasticsearch — Luanne Misquitta and Alessandro Negro, GraphAware

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.