This Week in Neo4j: Knowledge Graphs, NeoDash, Automation, Web Analysis and more


Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
This week features another tutorial to create knowledge graphs with LLMs, an overview of the new features added to NeoDash 2.4, some thoughts on how to use graphs for automation and a GitHub repo for taint analysis.

For beginners, I rotated a new set of links below. Let me know if you find these useful.

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I hope you enjoy this issue,
Alexander Erdl

 
COMING UP NEXT WEEK!
GETTING STARTED WITH GRAPHS

Divya is a hands-on technologist passionate about solving real-world problems with an interdisciplinary approach. Currently, she switches between providing data science/AI consulting services to organisations and teaching adult learners.
Connect with her on LinkedIn.

In her session at NODES “Whodunit? Getting Answers from the HK Legal Information Institute Text” Divya presents an analysis of Hong Kong judicial verdicts using LLMs and Graph databases to respond to questions using unstructured text from judicial verdicts, especially femicide cases, as context. She uses the answers and the original text to form a knowledge graph with Neo4j.


Divya Venkatraman

 
KNOWLEDGE GRAPH: Constructing a knowledge graph from text with Large Language Models (LLMs)

Diana Ow describes her journey in utilising Large Language Models (LLMs) for web development, focusing on extracting and structuring information for applications like Q&A chatbots and using LLMs with Knowledge Graphs to enhance their capabilities.
 
NEODASH: NeoDash 2.4: Unleashing the Power of Neo4j Graph Dashboards
NeoDash is a tool for building interactive graph dashboards, which has been enhanced with features like custom visualisations, 3D graphs, interactivity, and extensions to maximise the utility of Neo4j. Niels de Jong celebrates the release of NeoDash 2.4 with an overview of new capabilities to improve graph dashboards further, focusing on 3D graph visualisation, interactive forms, and customisable interfaces through extensions.    

AUTOMATION: Using Graph technology in Industrial Automation?
Dylan DuFresne describes his experience in automating over 800,000 unique paths in a complex system using a combination of Arrows.app, Neo4j, and custom Python pathfinding logic, resulting in a powerful proof of concept that efficiently documented all possible paths between two points in the system.
WEB ANALYSIS: FlowMate

FlowMate, a BurpSuite extension, introduces taint analysis to web applications by monitoring parameter appearances in responses. Its key features include tracking parameter values, storing data in a local Neo4j instance, integrated Neo4j browser for visualisation, session management within the plugin, and automatic audit steps for generating Findings.