Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
In this presentation, you’ll learn how companies are building Knowledge Graphs with Neo4j and the Hume platform in order to surface previously undiscoverable insights. We’ll go over the process of analysing unstructured data using Machine Learning techniques and how graphs are a wonderful representation for storing Knowledge, making it naturally connectable. Lastly, a Graph Visualisation demonstration will take place, showing new insights discovered from the results of the previous operations.
Are you interested in learning about how machine learning can be leveraged to build a knowledge graph, enabling businesses to differentiate themselves and thrive in today’s competitive marketplace? In this talk, we’ll show you how computer vision, natural language processing and understanding, knowledge enrichment, and graph-native algorithms can be combined to extract valuable insights from various unstructured data sources. Whether you’re a business owner looking to gain a competitive edge or a developer looking to expand your skillset, this talk is sure to be of interest to you.
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.
Alessandro Negro, Chief Scientist at GraphAware, delivers a presentation called Using Knowledge Graphs to predict customer needs, improve product quality and save costs during the SmartData Summit 2019 in Dubai.
Dr. Alessandro Negro, Chief Scientist at GraphAware, presents on knowledge graphs at GraphTour DC.
Discover how to turn your unstructured data silos into valuable knowledge graphs with the help of expert insights from Dr. Alessandro Negro. During his presentation at GraphTour DC, Dr. Negro shares valuable tips and strategies for converting unstructured data into useful knowledge that can inform decision-making and drive better outcomes. Whether you’re looking to extract new insights from large volumes of data or need to quickly analyze data in real-time, the strategies and techniques shared in this presentation can help you unlock the full potential of your data and transform it into a valuable asset. Don’t miss out on this opportunity to learn from an expert and take your data analysis to the next level.
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.
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.
In 2016, 25% of web searches on Android were made by voice and this percentage is predicted to double by 2018. From Amazon Alexa to Google Home, smartwatches and in-car systems, touch is no longer the primary user interface. In this talk, Alessandro and Christophe will demonstrate how graphs and machine learning are used to create an extracted and enriched graph representation of knowledge from text corpus and other data sources. This representation will then be used to map user intents made by voice to an entry point in this Neo4j backed knowledge graph. Every user interaction will then have to be taken into account at any further steps and we will highlight why graphs are an ideal data structure for keeping an accurate representation of a user context in order to avoid what is called machine or bot amnesia. The speakers will then conclude the session by explaining about how recommendations algorithms are used to predict next steps of the user’s journey.