string metric

{{Short description|Metric that measures the distance between two strings of text}}

{{redirect|String distance|the distance between strings and the fingerboard in musical instruments|Action (music)}}

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.{{cite book

| last = Lu

| first = Jiaheng | title = Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data | chapter = String similarity measures and joins with synonyms |display-authors=etal

| year=2013

| pages=373–384

| chapter-url=https://dl.acm.org/citation.cfm?id=2465313| doi = 10.1145/2463676.2465313

| isbn = 9781450320375

| s2cid = 2091942 }} 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).{{cite journal

| last = Navarro

| first = Gonzalo

| title = A guided tour to approximate string matching

| journal = ACM Computing Surveys |volume=33 |issue=1

| year=2001

| pages=31–88

| doi=10.1145/375360.375365| hdl=10533/172862

| s2cid = 207551224

| hdl-access=free

}} 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,{{cite journal |author1=Shlomi Dolev | last2=Mohammad |first2=Ghanayim |last3=Alexander |first3=Binun |last4=Sergey |first4=Frenkel |last5=Yeali |first5=S. Sun |title=Relationship of Jaccard and edit distance in malware clustering and online identification |journal=16th IEEE International Symposium on Network Computing and Applications |date=2017 |pages=369–373}} and semantic knowledge integration.