Glossary

Fuzzy matching

What is Fuzzy matching?

Fuzzy matching finds entries that are similar but not exactly identical – for example because of typos, abbreviations or different spellings. It relies on similarity measures such as the Levenshtein distance, which counts how many characters need to be changed. A typical use is finding duplicates in address and customer data: ‘Smith Ltd, 5 High St’ and ‘Smith Limited, 5 High Street’ are recognised as probably the same record.

How KI.ordo helps

The KI.ordo template ‘Clean up a list’ standardises spellings and flags possible duplicates, including entries with similar spellings.

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Matching templates

Clean up a listStandardises spellings, spaces, capitalisation and date formats, flags possible duplicates and missing details, and logs every change.View template →

Topics

Data cleansing
Duplicates
Text matching

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Related terms

Data cleansing

Data cleansing is the detection and correction of incorrect, duplicate or inconsistent entries in data sets. Typical steps include standardising spellings and date formats, removing superfluous spaces and merging duplicates such as 'Smith Ltd' and 'Smith Limited'. Clean data is a prerequisite for reliable analyses, mailings and AI applications.

data quality
duplicates
+1

Template

Data validation

Data validation checks whether data is complete, correctly formatted and plausible before it is processed further. Typical checks cover mandatory fields, the format of email addresses, postcodes or dates, and logical contradictions – such as a delivery date before the order date. This catches errors early instead of letting them carry over into reports, invoices or AI analyses.

data quality
checks
+1

Template

Did you mean (DYM)

“Did you mean” (DYM) is a search feature that suggests a corrected query when the input appears to be misspelt or unusual. It relies on similarity measures between words, language models and analysis of frequent searches. Example: someone typing “coffe machne” into an online shop sees “Did you mean: coffee machine?”. The feature reduces empty result pages and thus improves the search experience.

search
spelling correction
+1

Structured data

Structured data follows a fixed schema with clearly named fields, such as tables, databases or JSON objects containing details like name, price and date. Machines can read, filter and process it reliably – unlike free text or images. On websites, schema.org markup describes products, events or FAQs, for example, so that search engines and AI systems understand the content unambiguously. AI can also extract structured data from unstructured text.

data
JSON
+1