{"id":"https://openalex.org/W2000130621","doi":"https://doi.org/10.1145/1233341.1233453","title":"A comparative experiment on record match algorithms","display_name":"A comparative experiment on record match algorithms","publication_year":2007,"publication_date":"2007-03-23","ids":{"openalex":"https://openalex.org/W2000130621","doi":"https://doi.org/10.1145/1233341.1233453","mag":"2000130621"},"language":"en","primary_location":{"id":"doi:10.1145/1233341.1233453","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1233341.1233453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th annual southeast regional conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101796091","display_name":"Yan Liang","orcid":"https://orcid.org/0000-0003-2217-5357"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yan Liang","raw_affiliation_strings":["University of Alabama, Tuscaloosa, AL"],"affiliations":[{"raw_affiliation_string":"University of Alabama, Tuscaloosa, AL","institution_ids":["https://openalex.org/I17301866"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101796091"],"corresponding_institution_ids":["https://openalex.org/I17301866"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.12972015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9532999992370605,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.930400013923645,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7118426561355591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5657748579978943},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5517857670783997},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5232728719711304},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5143588781356812},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5068722367286682},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.495889276266098},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4282039999961853},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4205837845802307},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4057800769805908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7118426561355591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5657748579978943},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5517857670783997},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5232728719711304},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5143588781356812},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5068722367286682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.495889276266098},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4282039999961853},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4205837845802307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4057800769805908},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1233341.1233453","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1233341.1233453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th annual southeast regional conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W203696055","https://openalex.org/W2073471108","https://openalex.org/W2550120957","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2382043075","https://openalex.org/W2118758177","https://openalex.org/W2809151339","https://openalex.org/W4330338194","https://openalex.org/W2153520307","https://openalex.org/W2358294942","https://openalex.org/W4367460280"],"abstract_inverted_index":{"With":[0],"the":[1,5,10,53,59],"constantly":[2],"expansion":[3],"of":[4,7,12,26,52],"interconnectedness":[6],"database":[8],"systems,":[9],"importance":[11],"record":[13],"match":[14],"technologies":[15],"has":[16],"been":[17],"widely":[18],"recognized.":[19],"This":[20],"paper":[21],"made":[22],"an":[23],"experimental":[24],"comparison":[25],"a":[27,31,50],"distance-based":[28],"algorithm":[29],"and":[30,42,49],"Na\u00efve":[32],"Bayesian":[33],"Classifier":[34],"Algorithm":[35],"in":[36,58],"supervised":[37],"learning":[38],"on":[39],"precision,":[40],"recall":[41],"f-measure.":[43],"The":[44],"experiment":[45,60],"results":[46],"were":[47],"reported":[48],"subset":[51],"most":[54],"critical":[55],"parameters":[56],"involved":[57],"was":[61],"analyzed.":[62]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}