‘The amount of savings in time and effort [the search optimization] can deliver for our home offices, for our customers, is incredible.’
--Mayank Gupta, SVP for data, LPL Financial
Vlasta Kus talked about the advantages of graph-based natural language processing (NLP) using a public NASA dataset as example. From his abstract: “[…] we are building a platform (from large part open-source) that integrates Neo4j and NLP (such as Named Entity Recognition, sentiment analysis, word embeddings, LDA topic extraction), and we test and develop further related features and tools, lately, for example, integrating Neo4j and Tensorflow for employing deep learning techniques (such as deep auto-encoders for automatic text summarisation).”
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.
View the slides from Dr. Alessandro Negro’s presentation at GraphTour DC on how to convert unstructured data silos into powerful knowledge 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.
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.
View Christophe’s slides from the GraphTour Meetup that took place March 1, 2018.
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 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.