{"id":"https://openalex.org/W2981342983","doi":"https://doi.org/10.1080/13658816.2019.1681431","title":"A deep learning architecture for semantic address matching","display_name":"A deep learning architecture for semantic address matching","publication_year":2019,"publication_date":"2019-10-24","ids":{"openalex":"https://openalex.org/W2981342983","doi":"https://doi.org/10.1080/13658816.2019.1681431","mag":"2981342983"},"language":"en","primary_location":{"id":"doi:10.1080/13658816.2019.1681431","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2019.1681431","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Geographical Information Science","raw_type":"journal-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/A5009887094","display_name":"Yue Lin","orcid":"https://orcid.org/0000-0001-8568-7734"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yue Lin","raw_affiliation_strings":["Department of Geography, The Ohio State University, Columbus, OH, USA","School of Resource and Environmental Sciences, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-8568-7734","affiliations":[{"raw_affiliation_string":"Department of Geography, The Ohio State University, Columbus, OH, USA","institution_ids":["https://openalex.org/I52357470"]},{"raw_affiliation_string":"School of Resource and Environmental Sciences, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046037329","display_name":"Mengjun Kang","orcid":"https://orcid.org/0000-0003-3518-5853"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengjun Kang","raw_affiliation_strings":["School of Resource and Environmental Sciences, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-3518-5853","affiliations":[{"raw_affiliation_string":"School of Resource and Environmental Sciences, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020516263","display_name":"Yuyang Wu","orcid":"https://orcid.org/0000-0001-8592-0305"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyang Wu","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041093991","display_name":"Qingyun Du","orcid":"https://orcid.org/0000-0003-4615-2029"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyun Du","raw_affiliation_strings":["School of Resource and Environmental Sciences, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-4615-2029","affiliations":[{"raw_affiliation_string":"School of Resource and Environmental Sciences, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100715767","display_name":"Tao Liu","orcid":"https://orcid.org/0000-0001-7098-4285"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Liu","raw_affiliation_strings":["Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046037329"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":8.5506,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.97026253,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"34","issue":"3","first_page":"559","last_page":"576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10757","display_name":"Geographic Information Systems Studies","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7571629285812378},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6360509395599365},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.6037018299102783},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5790119767189026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5376841425895691},{"id":"https://openalex.org/keywords/semantic-matching","display_name":"Semantic matching","score":0.5353463292121887},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5119682550430298},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5013141632080078},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5009479522705078},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.467768132686615},{"id":"https://openalex.org/keywords/geocoding","display_name":"Geocoding","score":0.44562047719955444},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4331724941730499},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41947492957115173},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3980366885662079},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37887802720069885},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10934069752693176}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7571629285812378},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6360509395599365},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.6037018299102783},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5790119767189026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5376841425895691},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.5353463292121887},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5119682550430298},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5013141632080078},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5009479522705078},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.467768132686615},{"id":"https://openalex.org/C42629822","wikidata":"https://www.wikidata.org/wiki/Q1346408","display_name":"Geocoding","level":2,"score":0.44562047719955444},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4331724941730499},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41947492957115173},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3980366885662079},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37887802720069885},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10934069752693176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/13658816.2019.1681431","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2019.1681431","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Geographical Information Science","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8600000143051147,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W189514790","https://openalex.org/W569478347","https://openalex.org/W1615991656","https://openalex.org/W1647671624","https://openalex.org/W1809873675","https://openalex.org/W1975652677","https://openalex.org/W2034190452","https://openalex.org/W2064675550","https://openalex.org/W2097998348","https://openalex.org/W2141699138","https://openalex.org/W2144981456","https://openalex.org/W2153635508","https://openalex.org/W2372060463","https://openalex.org/W2534538876","https://openalex.org/W2592549418","https://openalex.org/W2593390416","https://openalex.org/W2604272474","https://openalex.org/W2751985240","https://openalex.org/W2757470890","https://openalex.org/W2765285316","https://openalex.org/W2785232091","https://openalex.org/W2795684330","https://openalex.org/W2805365628","https://openalex.org/W2900849519","https://openalex.org/W2901833745","https://openalex.org/W2911964244","https://openalex.org/W2921188025","https://openalex.org/W2935841898","https://openalex.org/W4214671568","https://openalex.org/W4246089517","https://openalex.org/W4249550832","https://openalex.org/W4251489445","https://openalex.org/W6607642809"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W64465677"],"abstract_inverted_index":{"Address":[0,139],"matching":[1,37,65,151,163],"is":[2],"a":[3,28,104,161],"crucial":[4],"step":[5],"in":[6,13],"geocoding,":[7],"which":[8],"plays":[9],"an":[10,63],"important":[11],"role":[12],"urban":[14],"planning":[15],"and":[16,48,111,141,173,182],"management.":[17],"To":[18,120],"date,":[19],"the":[20,42,54,73,82,87,98,122,125,130,137,143,157,177,187],"unprecedented":[21],"development":[22],"of":[23,31,45,124,147,180],"location-based":[24],"services":[25],"has":[26],"generated":[27],"large":[29],"amount":[30],"unstructured":[32,55,166],"address":[33,36,46,56,64,77,88,134,150,167],"data.":[34,57],"Traditional":[35],"methods":[38],"mainly":[39],"focus":[40],"on":[41,68,186],"literal":[43],"similarity":[44,75],"records":[47,89],"are":[49],"therefore":[50],"not":[51],"applicable":[52],"to":[53,71,85,108,114],"In":[58],"this":[59],"study,":[60],"we":[61,80,96,128],"introduce":[62],"method":[66,159],"based":[67],"deep":[69,105],"learning":[70],"identify":[72],"semantic":[74],"between":[76],"records.":[78],"First,":[79],"train":[81],"word2vec":[83],"model":[84,102,131],"transform":[86],"into":[90],"their":[91],"corresponding":[92],"vector":[93],"representations.":[94],"Next,":[95],"apply":[97],"enhanced":[99],"sequential":[100],"inference":[101],"(ESIM),":[103],"text-matching":[106],"model,":[107],"make":[109],"local":[110],"global":[112],"inferences":[113],"determine":[115],"if":[116],"two":[117],"addresses":[118],"match.":[119],"evaluate":[121],"accuracy":[123,164],"proposed":[126,158],"method,":[127],"fine-tune":[129],"with":[132,145,169],"real-world":[133],"data":[135],"from":[136],"Shenzhen":[138],"Database":[140],"compare":[142],"outputs":[144],"those":[146],"several":[148],"popular":[149],"methods.":[152],"The":[153],"results":[154],"indicate":[155],"that":[156],"achieves":[160],"higher":[162],"for":[165],"records,":[168],"its":[170],"precision,":[171],"recall,":[172],"F1":[174],"score":[175],"(i.e.,":[176],"harmonic":[178],"mean":[179],"precision":[181],"recall)":[183],"reaching":[184],"0.97":[185],"test":[188],"set.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
