Taxamatch, an Algorithm for Near (‘Fuzzy’) Matching of Scientific Names in Taxonomic Databases
Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis o...
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| Veröffentlicht in: | PloS one Jg. 9; H. 9; S. e107510 |
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23.09.2014
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| Abstract | Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis of the nature of the problem from first principles, reviews some available algorithmic approaches, and describes Taxamatch, an improved name matching solution for this information domain. Taxamatch employs a custom Modified Damerau-Levenshtein Distance algorithm in tandem with a phonetic algorithm, together with a rule-based approach incorporating a suite of heuristic filters, to produce improved levels of recall, precision and execution time over the existing dynamic programming algorithms n-grams (as bigrams and trigrams) and standard edit distance. Although entirely phonetic methods are faster than Taxamatch, they are inferior in the area of recall since many real-world errors are non-phonetic in nature. Excellent performance of Taxamatch (as recall, precision and execution time) is demonstrated against a reference database of over 465,000 genus names and 1.6 million species names, as well as against a range of error types as present at both genus and species levels in three sets of sample data for species and four for genera alone. An ancillary authority matching component is included which can be used both for misspelled names and for otherwise matching names where the associated cited authorities are not identical. |
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| AbstractList | Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis of the nature of the problem from first principles, reviews some available algorithmic approaches, and describes Taxamatch, an improved name matching solution for this information domain. Taxamatch employs a custom Modified Damerau-Levenshtein Distance algorithm in tandem with a phonetic algorithm, together with a rule-based approach incorporating a suite of heuristic filters, to produce improved levels of recall, precision and execution time over the existing dynamic programming algorithms n-grams (as bigrams and trigrams) and standard edit distance. Although entirely phonetic methods are faster than Taxamatch, they are inferior in the area of recall since many real-world errors are non-phonetic in nature. Excellent performance of Taxamatch (as recall, precision and execution time) is demonstrated against a reference database of over 465,000 genus names and 1.6 million species names, as well as against a range of error types as present at both genus and species levels in three sets of sample data for species and four for genera alone. An ancillary authority matching component is included which can be used both for misspelled names and for otherwise matching names where the associated cited authorities are not identical. Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis of the nature of the problem from first principles, reviews some available algorithmic approaches, and describes Taxamatch, an improved name matching solution for this information domain. Taxamatch employs a custom Modified Damerau-Levenshtein Distance algorithm in tandem with a phonetic algorithm, together with a rule-based approach incorporating a suite of heuristic filters, to produce improved levels of recall, precision and execution time over the existing dynamic programming algorithms n-grams (as bigrams and trigrams) and standard edit distance. Although entirely phonetic methods are faster than Taxamatch, they are inferior in the area of recall since many real-world errors are non-phonetic in nature. Excellent performance of Taxamatch (as recall, precision and execution time) is demonstrated against a reference database of over 465,000 genus names and 1.6 million species names, as well as against a range of error types as present at both genus and species levels in three sets of sample data for species and four for genera alone. An ancillary authority matching component is included which can be used both for misspelled names and for otherwise matching names where the associated cited authorities are not identical.Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis of the nature of the problem from first principles, reviews some available algorithmic approaches, and describes Taxamatch, an improved name matching solution for this information domain. Taxamatch employs a custom Modified Damerau-Levenshtein Distance algorithm in tandem with a phonetic algorithm, together with a rule-based approach incorporating a suite of heuristic filters, to produce improved levels of recall, precision and execution time over the existing dynamic programming algorithms n-grams (as bigrams and trigrams) and standard edit distance. Although entirely phonetic methods are faster than Taxamatch, they are inferior in the area of recall since many real-world errors are non-phonetic in nature. Excellent performance of Taxamatch (as recall, precision and execution time) is demonstrated against a reference database of over 465,000 genus names and 1.6 million species names, as well as against a range of error types as present at both genus and species levels in three sets of sample data for species and four for genera alone. An ancillary authority matching component is included which can be used both for misspelled names and for otherwise matching names where the associated cited authorities are not identical. |
| Audience | Academic |
| Author | Rees, Tony |
| AuthorAffiliation | CSIRO Oceans and Atmosphere Flagship, Hobart, Tasmania, Australia Consiglio Nazionale delle Ricerche (CNR), Italy |
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| Author_xml | – sequence: 1 givenname: Tony surname: Rees fullname: Rees, Tony |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25247892$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1093_sysbio_syw023 crossref_primary_10_3897_BDJ_6_e28073 crossref_primary_10_3897_BDJ_4_e8080 crossref_primary_10_3897_zookeys_849_34763 crossref_primary_10_1371_journal_pone_0194599 crossref_primary_10_1002_widm_1394 crossref_primary_10_1016_j_cbpa_2024_111696 crossref_primary_10_3897_BDJ_4_e7971 crossref_primary_10_1111_nph_18919 crossref_primary_10_3390_plants10050974 crossref_primary_10_3897_BDJ_7_e29626 crossref_primary_10_1002_aps3_11388 crossref_primary_10_1093_bioadv_vbad144 crossref_primary_10_3233_SW_160220 crossref_primary_10_3897_zookeys_550_9546 crossref_primary_10_1038_s41598_024_66912_2 crossref_primary_10_3390_electronics9101670 crossref_primary_10_1111_nph_18961 crossref_primary_10_1371_journal_pone_0268162 crossref_primary_10_1186_s12859_019_2874_6 crossref_primary_10_1371_journal_pone_0175277 crossref_primary_10_7717_peerj_16896 crossref_primary_10_1002_ece3_71852 crossref_primary_10_3897_rio_3_e13589 crossref_primary_10_1155_2020_8896659 crossref_primary_10_1371_journal_pone_0146894 crossref_primary_10_1111_2041_210X_13802 crossref_primary_10_7717_peerj_cs_465 crossref_primary_10_1111_jvs_12432 crossref_primary_10_1111_jvs_12419 crossref_primary_10_1111_2041_210X_13440 crossref_primary_10_1186_s12859_023_05588_3 |
| Cites_doi | 10.1186/1471-2105-14-16 10.1145/356827.356830 10.1016/j.ijmedinf.2010.09.005 10.1109/ICDMW.2006.2 10.1642/0004-8038(2003)120[0923:FSTTAO]2.0.CO;2 10.1111/j.2041-210X.2012.00232.x 10.1002/spe.4380250307 10.1016/0306-4573(83)90045-6 10.1023/A:1009761603038 10.1145/375360.375365 10.1145/146370.146380 10.1002/ece3.529 10.1126/science.11542058 10.1016/0306-4573(83)90046-8 10.1145/363958.363994 10.1007/s10032-002-0082-8 10.1016/j.envsoft.2009.11.014 10.1145/321796.321811 10.1016/j.tree.2010.09.004 10.1145/321879.321880 10.1108/EUM0000000007161 10.1017/CBO9780511809071 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Tony Rees. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Tony Rees 2014 Tony Rees |
| Copyright_xml | – notice: COPYRIGHT 2014 Public Library of Science – notice: 2014 Tony Rees. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2014 Tony Rees 2014 Tony Rees |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: TR. Performed the experiments: TR. Analyzed the data: TR. Contributed reagents/materials/analysis tools: TR. Wrote the paper: TR. Competing Interests: The author has declared that no competing interests exist. |
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| Title | Taxamatch, an Algorithm for Near (‘Fuzzy’) Matching of Scientific Names in Taxonomic Databases |
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