Turn spreadsheets into knowledge graphs — visually and without ontology expertise.
A drag-and-drop mapping tool for RDF/OWL ontologies with built-in RDF export for our own data.

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What is Table2Knowledge Studio?

Table2Knowledge Studio is a localhost web application that lets you map tabular data (CSV, TSV, Excel) onto RDF/OWL ontologies through an intuitive visual interface. You build a conceptual graph by dragging ontology classes onto a canvas, connect them with properties, assign table columns to nodes, and export the result as RDF — ready for your triplestore.

It was originally developed for archaeological data management using CIDOC CRM, but works with any RDF/OWL ontology.

Key Features

• Visual graph builder — Drag-and-drop ontology classes, draw property connections, build your mapping visually
• Ontology-aware — Loads TTL/RDF/OWL files, browses class hierarchies, suggests valid properties with domain/range inference including owl:inverseOf
• Table mapping — Load CSV/TSV/XLSX files, drag column headers onto graph nodes to assign IDs and labels
• Dot-One properties — Visual support for RDF-star annotations (e.g. typing a relationship: << SU1 AP11_has_physical_relation_to SU2 >> P2_has_type "above")
• Named Graphs — Group nodes into named graphs (I4_Proposition_Set) with visual bounding boxes
• Multi-format RDF export — TriG, N-Quads, Turtle, RDF/XML, N-Triples, JSON-LD — with proper RDF-star syntax for dot-one properties
• RDF Pipeline — Integrated 3-step workflow: Export → Ontotext Refine → GraphDB
• GraphML export — yEd-compatible with colors, positions, and edge labels
• Image export — Publication-ready PNG (2× resolution) and editable SVG
• Graph verification — Checks for missing mappings, ID/label swaps, orphan nodes, widening warnings
• CIDOC CRM color convention — Nodes are automatically colored by their CRM superclass

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Quick Start

Prerequisites

• Python 3.10+ with pip
• Node.js 18+ with npm

One-Command Setup (only needed once)

Windows:

setup.bat

Linux / macOS:

chmod +x setup.sh
./setup.sh

This will:

1. Create a Python virtual environment and install dependencies
2. Install frontend npm packages

One-Command Start

Windows:

start.bat

Linux / macOS:

chmod +x start.sh
./start.sh

This will:

1. Start the backend (FastAPI on port 8000)
2. Wait for the backend to be ready
3. Start the frontend (Vite on port 3000)
4. Open your browser

Manual Start

# Backend
cd backend
python -m venv .venv
.venv/Scripts/activate    # Windows
# source .venv/bin/activate  # Linux/Mac
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

# Frontend (separate terminal)
cd frontend
npm install
npm run dev

Then open http://localhost:3000.

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How It Works

1. Load an Ontology

Upload one or more TTL/RDF/OWL ontology files. The backend parses them and builds an inference cache for class hierarchies, property domains/ranges, and owl:inverseOf relationships.

2. Build Your Conceptual Graph

Use the Triple Explorer (left panel) to browse the ontology:

• ① Subject — Select a class (e.g. E27_Site)
• ② Predicate — Pick a property (e.g. P89_falls_within)
• ③ Object — Choose the range class (e.g. E53_Place)

Drag classes onto the canvas to create nodes. Connect them by drawing edges between handles, or let the tool auto-connect when you drop an object while a subject is selected.

3. Map Your Table Data

Switch to the Tables tab, load your CSV/TSV/XLSX file, and drag column headers onto nodes:

• Upper drop zone → Label column (display name)
• Lower drop zone → ID column (unique identifier)

4. Export

Choose your export format:

Export	Description
RDF (TriG, Turtle, etc.)	Direct 1-click RDF export with RDF-star support
RDF Pipeline	3-step workflow via Ontotext Refine → GraphDB
GraphML	yEd-compatible graph with colors and positions
PNG	Publication-ready image at 2× resolution
SVG	Scalable vector graphic, editable in Illustrator/Inkscape

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Dot-One Properties (RDF-star)

Table2Knowledge supports annotating relationships — for example, typing a stratigraphic relation:

"SU1002 has a physical relation to SU1001, and that relation has type 'below'"

In RDF-star syntax:

<< oeai:SU1002 crmarchaeo:AP11_has_physical_relation_to oeai:SU1001 >>
    crm:P2_has_type oeai:below .

To create a dot-one: select an edge in the graph, then drag an ontology class onto it. A visual midpoint appears on the edge showing the annotation.

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Named Graphs

Group nodes into named graphs (useful for CIDOC CRM's I4_Proposition_Set or any named graph context):

1. Select nodes in the graph (Shift+Click or drag a selection box)
2. Open the Graphs panel
3. Enter a label and click Selection → Graph

Click on a named graph entry to highlight its nodes with a colored bounding box and zoom to fit.

Named graphs are exported in TriG and N-Quads formats.

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Technology Stack

Layer	Technology
Frontend	React 18, Vite 5, ReactFlow 11, Lucide Icons, html-to-image
Backend	Python, FastAPI, rdflib, pandas, openpyxl
Ontology Support	RDF/OWL with RDFS inference, owl:inverseOf, owl:unionOf
Export	TriG, N-Quads, Turtle, RDF/XML, N-Triples, JSON-LD, GraphML, PNG, SVG
Optional	Ontotext Refine + GraphDB (for the RDF Pipeline workflow)

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Project Structure

table2knowledge/
├── backend/
│   ├── main.py              # FastAPI server + rdflib inference engine
│   └── requirements.txt
├── frontend/
│   ├── public/logo.png      # Application logo
│   ├── src/
│   │   ├── App.jsx           # Main application component
│   │   ├── components/       # UI components (modals, panels, nodes)
│   │   ├── utils/            # Export logic, API calls, color scheme
│   │   └── index.css         # Light theme with IBM Plex typography
│   └── package.json
├── start.bat                 # Windows launcher
├── start.sh                  # Linux/Mac launcher
└── README.md

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Configuration

Prefix Manager

All namespace prefixes must be defined for correct RDF URI expansion. The Prefix Manager:

• Auto-detects prefixes from loaded ontologies
• Scans table data for prefixed values and warns about undefined prefixes
• Manages the Data ID Prefix (prepended to all row IDs, e.g. oeai:)

Widening

Enable Widening in the toolbar to also see properties defined on subclasses of your selected subject. Useful when working with broad classes like E18_Physical_Thing. Widened properties are highlighted in amber.

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CIDOC CRM Support

While Table2Knowledge works with any RDF/OWL ontology, it has built-in support for CIDOC CRM:

• Color coding — Nodes are colored by their CRM anchor class (e.g. brown for Physical Things, blue for Temporal Entities, pink for Actors)
• Label preference — German rdfs:label translations are preferred, with fallback to English
• CRM extensions — Supports CRMarchaeo, CRMsci, LRMoo and other CIDOC extensions
• Auto-prefixing — CRM property patterns (P1_, AP3_, SP5_ etc.) are auto-resolved to crm: prefix

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Saving & Loading Projects

Projects are saved as JSON files containing:

• All nodes with positions, colors, mapped columns, and table data
• All edges with properties, join keys, and dot-one annotations
• Named graph definitions
• Prefix map and configuration

This means you can save your work, close the application, and resume later — even without the original table files loaded.

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License

GPL 3.0

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Acknowledgments and Credits

RDF-Pipeline incl. all Scripts for Ontotext Refine and GraphDB: Gerald Hiebel, University of Innsbruck, Institute of Archaeology and Digital Science Centre Gerald.Hiebel@uibk.ac.at; https://www.uibk.ac.at/archaeologien/institut/mitarbeiter/gerald-hiebel/gerald_hiebel.html; OrcID: https://orcid.org/0000-0002-3799-8391

Ontotext Refine Client: https://github.com/Ontotext-AD/ontorefine-client — Apache-2.0 license

Developed for digital archaeology and cultural heritage data management. The CIDOC CRM color scheme follows established conventions in the CRM community.