Term Indexing and Matching Overview Examples

🧭 Quickstart: Exact & Single-Edit Typos

This section demonstrates how the Term Index Matcher finds exact matches and small typos using a simple Levenshtein comparison.

• Levenshtein distance handles insertions, deletions, and substitutions.

• Perfect for testing single-edit errors or near-identical names.

• Works best on short, clean strings like first names.

🔤 Character Normalization

This example shows how Character Mapper normalization improves match accuracy when dealing with diacritics or special characters.

• Maps accented letters (Á→A, ö→oe, é→e).

• Ensures consistent indexing and querying across scripts.

• Ideal for multilingual or international datasets.

Reference Names:

• MÁRÍA

• Jörg

• François

💾 Index Writing

🔐 How to Get Your License

Use this node to request and register your exorbyte matchmaker license before running any toolbox nodes.

1. Choose Demo (30 days) or Production.

2. Enter your email (and Customer Token if production).

3. Execute the node — it sends a secure request to exorbyte team.

4. If offline, manually email the request file toknime-node-license@exorbyte.com.

5. When you receive the .lic file, reopen the node → Use available license fileand run the node → run License Activator.

⚠️ Each KNIME installation or Hub environment needs its own license.

👉 See full workflow guide: How to license exorbyte Extension

🅰 Name Variant Matching

This section demonstrates how the Term Index Matcher can detect name variants and associate them with their canonical forms using Levenshtein similarity.

• Index the base names using Term Indexer.

• Match the variants against the indexed base names with the Term Index Matcher using the Levenshtein algorithm.

📥 Index Reading