CACrown ArchivesThe cinema collection
Menu
Research dossier · General Reference

String metric

metric that measures the distance between two strings of text

Cross-disciplinary reference desk with index cards, atlas, dictionary and catalogue
General referenceInterpretive dossier study · Crown Archives visual atlas
Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionAug 12, 2024
Entity authorityQ9050568
Source-derived summary

In mathematics and computer science, a string metric (also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings for approximate string matching or comparison and in fuzzy string searching. A requirement for a string metric (e.g. in contrast to string matching) is fulfillment of the triangle inequality. For example, the strings "Sam" and "Samuel" can be considered to be close. A string metric provides a number indicating an algorithm-specific indication of distance.

The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token, grammatical and character-based methods of statistical comparisons.

String metrics are used heavily in information integration and are currently used in areas including fraud detection, fingerprint analysis, plagiarism detection, ontology merging, DNA analysis, RNA analysis, image analysis, evidence-based machine learning, database data deduplication, data mining, incremental search, data integration, malware detection, and semantic knowledge integration.

List of string metrics

Levenshtein distance, or its generalization edit distance

Damerau–Levenshtein distance

Sørensen–Dice coefficient

Block distance or L1 distance or City block distance

Hamming distance

Simple matching coefficient (SMC)

Jaccard similarity or Jaccard coefficient or Tanimoto coefficient

Tversky index

Overlap coefficient

Variational distance

Hellinger distance or Bhattacharyya distance

Information radius (Jensen–Shannon divergence)

Skew divergence

Confusion probability

Tau metric, an approximation of the Kullback–Leibler divergence

Fellegi and Sunters metric (SFS)

Maximal matches

Grammar-based distance

TFIDF distance metric

There also exist functions which measure a dissimilarity between strings, but do not necessarily fulfill the triangle inequality, and as such are not metrics in the mathematical sense.

Editorial summary

Begin with the source’s own compact description: “String metric” is metric that measures the distance between two strings of text. The dossier treats that line as a proposition to test through String, metric and measures, not as a finished interpretation.

Editorial reviewA concise reference frame for defining the subject, testing terminology and identifying the institution closest to the evidence. The current 303-word lead offers orientation but no explicit four-digit date, so chronology should not be assumed. The selected authority fields contribute no independent date. For this dossier, String, metric and measures is the immediate research focus.
Editorial analysis

Why this record matters

The phrase “metric that measures the distance between two strings of text” supplies a clear boundary for inquiry. It also exposes the unanswered questions: who defined that boundary, when it became stable and which sources sit outside it.

Evidence profile

Named sources, stable identifiers and responsible institutions provide the strongest route from overview to verifiable evidence. The source revision retrieved here is dated Aug 12, 2024. The linked authority identifier is Q9050568. None of the 0 selected statements returned an explicit reference.

Critical limits

The absence of detail may reflect summary conventions rather than a lack of surviving documentation. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

How to read it

Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.

Best used for
  • Subject orientation
  • Search vocabulary
  • Locating named sources
Verify next

The closest primary source, responsible institution and strongest cited specialist reference.

Three-step research path

  1. Establish the record: confirm the title “String metric”, its source revision and the description used here.
  2. Expand the search: follow String metric primary sources, String metric archive and String research across catalogues and specialist indexes.
  3. Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.

Questions for further research

  1. Which source most directly establishes the central claim about “String metric”?
  2. What terminology or title could unlock a more precise catalogue search?
  3. Which cited source is closest to the event, object or claim?
Subject index

Search terms from this dossier

Source & attribution

This entry incorporates text from String metric” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.