Entity disambiguation is a critical challenge in today’s data-driven world, where terms like “Apple” can refer to both a fruit and a tech giant. Research highlights that poor entity resolution impacts data quality and downstream analytics, yet traditional solutions often compromise between accuracy and speed. Developed by SINTEF within the enRichMyData project, Crocodile leverages advanced context management to deliver precise, scalable, and intelligent entity linking.
Why Entity Linking Matters More Than Ever
Modern data systems battle three core challenges:
- Ambiguity: “Apple” could be a fruit or trillion-dollar tech company
- Variability: “New York” vs “NYC” vs “The Big Apple”
- Scale: Processing millions of entities in real-time
Traditional solutions either sacrifice accuracy for speed or vice versa.
Meet Crocodile 🐊 – that finally brings surgical precision to entity linking.
The Crocodile Difference: Key Features
✅ Context-Sensitive Linking
Uses dynamic context windows rather than static rules
✅ Hybrid Architecture
MongoDB caching + async Python workers + TensorFlow ML
✅ Enterprise-Grade Tracking
Real-time progress monitoring with TraceThread technology
✅ Smart Caching
MongoCache drastically reduces API calls
✅ ML-Powered Enrichment
Machine learning models for cross-lingual/multi-modal matching.
🔗 Get Started
Crocodile is open-source and ready for use. Check out the GitHub repository to start enriching your tabular data today: