What to expect?
We are excited to be part of Neo4j’s GraphSummit Europe on October 16-17th, 2024, as we discover the latest innovations in graph technology.
Hear directly from companies, agencies and other users about their real-world successes using the power of graphs to drive business breakthroughs, even as data complexity grows.
Meet with Michal Bachman, GraphAware CEO & Founder, to discuss the latest advancements in graph analytics technology.
Looking forward to seeing you there!
Exhibition
Graph Powered Police Investigation
Stop by our booth to witness how Hume, powered by a graph database, revolutionises police investigations.
Our engineers will demonstrate how Hume seamlessly integrates disparate data sources, employs advanced analytics like temporal and geospatial analysis, and empowers investigators with intuitive visualisations. From identifying prime suspects to monitoring offenders and identifying threats, Hume streamlines the entire investigative process.
Analysts visiting our booth will experience the power of data linkage and complex querying firsthand, discovering how Hume, with its graph database backbone, can enhance their investigative efficiency.
Knowledge Graph Extraction from Unstructured Data Using GPT
At our booth, we will also showcase a groundbreaking paradigm shift with Large Language Models (LLMs) utilised for extracting knowledge graphs from diverse unstructured data sources such as documents, judicial reports, investigator reports, and social media posts.
Seamlessly integrated within the Hume data orchestration tool, LLMs empower data engineers to effortlessly integrate them into workflows, significantly reducing the time to acquire knowledge from months to mere minutes. Hume Orchestra harnesses the power of LLMs for entity extraction and relationship identification from text, sculpting a structured graph/network within a production system.
Creating Knowledge Graph from Phone Forensics
We will demonstrate the ingestion of forensic phone extractions and their transformation into a knowledge graph.
Based on information content and graph connectivity, we will select the most relevant target entities. Then, we will extract key information from these target phones using cutting-edge AI, including LLMs. Finally, we will enrich the data using OSINT, providing unique context on the target entities.
Seamlessly integrated within the Hume data orchestration tool, LLMs empower data engineers to effortlessly integrate them into workflows, significantly reducing the time to acquire knowledge from months to mere minutes. Hume Orchestra harnesses the power of LLMs for entity extraction and relationship identification from text, sculpting a structured graph/network within a production system.