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TuringDB Example Notebooks

This collection of notebooks demonstrates how to use TuringDB for real-world analytical examples. Each notebook focuses on a different domain and use case, from fraud detection to biological graph exploration, leveraging the performance and versioning capabilities of TuringDB. Explore the full set of notebooks on GitHub: github.com/turing-db/turingdb-examples

Domain: Finance & Fraud Detection

Paysim Financial Fraud Detection

Uses the synthetic PaySim dataset to simulate a transactional graph. Highlights how graph queries can uncover suspicious transaction patterns and how TuringDB supports fast multi-hop detection for fraud rings and mule accounts. Query examples:

Crypto Orbitaal Fraud Detection

Demonstrates risk analysis on crypto transaction data. Focuses on exploring wallets, exchanges, and fund flows to detect potential illicit activity through graph-based features such as suspicious loops and layered structures. Query examples:

Domain: Transport & Logistics

London Transport (TfL)

Models the London transport network as a graph. Investigates connections between stations, line transfers, and potential applications for routing or infrastructure planning using fast traversal queries. Query examples:

Supply Chain — ETO Chip Explorer

Illustrates how to model a manufacturing and supply chain graph. Tracks the flow of materials (e.g. ETO chips) through suppliers, batches, and final assemblies — enabling traceability, root cause analysis, and risk assessment. Query examples:

Domain: Healthcare & Life Sciences

Reactome Biological Pathways

Explores the Reactome knowledge base of biological pathways. Showcases how TuringDB can rapidly answer multi-hop queries on complex biological interactions such as gene regulation or protein-protein interaction cascades. Query examples:

Healthcare Knowledge Graph

Builds a patient-centric knowledge graph combining symptoms, diagnoses, treatments, and side effects. Demonstrates entity extraction, GML ingestion, and how TuringDB supports explainable AI on structured health data. Query examples:

CiteAb Antibody Data

Explores the CiteAb antibody database as a knowledge graph linking antibodies, publications, and suppliers. Demonstrates property-based filtering, edge traversal, and LLM-assisted natural language querying with an auto-generated Cypher system prompt. Query examples:

Domain: Corporate & Governance

French Corporate Network

Builds a corporate governance graph from the French public company registry (CAC 40 companies). Models board members, executive roles, and audit firms. Explores interlocking directorates (people sitting on multiple boards), non-executive roles, and media-corporate board overlaps through graph queries and interactive PyVis visualizations. Query examples:

Integrate RDF File

Shows how to convert an RDF/OWL ontology file into TuringDB’s JSONL format and load it as a graph. Covers subject–predicate–object triple flattening, label mapping, and querying the resulting semantic graph.

Running the Notebooks

Quick Start

Prerequisites

  • Python 3.13 or higher
  • uv package manager

Installation

  1. Clone the repository:
  2. Install dependencies:
  3. Start Jupyter Lab:
    This will start the Jupyter server and display output like:
    Copy the URL (starting with http://localhost:8888/lab?token=...) and paste it into your web browser to access Jupyter Lab.
  4. Open and run the example notebooks in examples/notebooks/public_version/