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.
Vlasta Kůs is Lead Data Scientist at GraphAware and presented at NODES2022. Public archives contain incredible amount of knowledge. In this session, we’ll cover a real use case of building a knowledge graph for the archive of a major foundation to help empower researchers (or business analysts) to access previously unavailable levels of insights. This archive, going up to a century back, contains detailed information about funded projects and conversations preceding them, budgets, research endeavors, and outcomes, as well as priceless knowledge about influence networks of foundation representatives, researchers, and students. A particular challenge was that the same events were described in multiple sources. The only way to leverage all of this knowledge was through the use of advanced analytics and machine learning. We will explore the technologies (including OCR, NLP, and graph data science) and complex pipelines employed to create this major knowledge graph.
Estelle Scifo is a Machine Learning Engineer at GraphAware and presented at NODES2022. Leverage Cypher map projections and Python dynamic typing to build an Object Graph Mapper for Neo4j. In this step-by-step session, you’ll learn how to get started on such a project, from defining the framework API to automatically building Cypher queries.
Graphs can be truly transformational for law enforcement agencies. Learn how a cutting-edge graph solution removes obstacles from the criminal intelligence process and increases its efficiency.
Dive deeper into Hume Orchestra, our data-driven orchestration tool, with our CTO, Christophe Willemsen.
Graphs are commonplace in investigative, intelligence, and law enforcement work. One of the primary advantages of a graph is to connect data from various data sources, digital and human, and maximize insights across deep and complex networks of connections, bringing them together in fusion centers for a centralized view of suspicious activities. For analysts, data quality and trust is key. The reliability, validity, and general consistency of data sources that contribute to forming real world fused entities is a factor that influences the analysts’ interpretation of events. This session talks about the challenges related to surfacing these aspects of data provenance and various approaches that can be employed to address them using Neo4j. We will touch on graph modeling, implications for data security, and how sources and information ratings can be effectively shared with analysts who need access to them.
Learn how graphs and Hume can help you tackle logistics challenges.
Learn how Hume, graph analytics and machine learning can be powerful tools to help you detect, investigate and prevent money laundering activities.
Fraud is becoming more and more complex. Learn how graphs and Hume can help you fight and detect fraud.
An introduction to Graph-Powered Machine Learning written by our very own Dr. Alessandro Negro. This book is an extraction of 60 combined years of experience in graphs, and explains how graphs and graph databases can serve machine learning projects.
GraphAware and Neo4j experts demonstrate how you can leverage knowledge graphs to help with compliance challenges.