{"id":"https://openalex.org/W4283372881","doi":"https://doi.org/10.14778/3529337.3529355","title":"A critical re-evaluation of neural methods for entity alignment","display_name":"A critical re-evaluation of neural methods for entity alignment","publication_year":2022,"publication_date":"2022-04-01","ids":{"openalex":"https://openalex.org/W4283372881","doi":"https://doi.org/10.14778/3529337.3529355"},"language":"en","primary_location":{"id":"doi:10.14778/3529337.3529355","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3529337.3529355","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://infoscience.epfl.ch/record/302684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077217656","display_name":"M. Leone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manuel Leone","raw_affiliation_strings":["EPFL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPFL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068517634","display_name":"Stefano Huber","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Huber","raw_affiliation_strings":["EPFL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPFL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103180781","display_name":"Akhil Arora","orcid":"https://orcid.org/0000-0002-8650-8213"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akhil Arora","raw_affiliation_strings":["EPFL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPFL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063933395","display_name":"Alberto Garc\u00eda-Dur\u00e1n","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alberto Garc\u00eda-Dur\u00e1n","raw_affiliation_strings":["EPFL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPFL","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101446790","display_name":"Robert West","orcid":"https://orcid.org/0000-0002-3984-1232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robert West","raw_affiliation_strings":["EPFL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPFL","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0714,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.94344516,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"15","issue":"8","first_page":"1712","last_page":"1725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9998999834060669,"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":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.960099995136261,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9556999802589417,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.8057556748390198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7543383836746216},{"id":"https://openalex.org/keywords/panacea","display_name":"Panacea (medicine)","score":0.7312921285629272},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5532464981079102},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4993557929992676},{"id":"https://openalex.org/keywords/de-facto","display_name":"De facto","score":0.4683261215686798},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4595981240272522},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4368496239185333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43427902460098267},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.433262437582016},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4248199760913849},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22821998596191406},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09412992000579834},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09012371301651001},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07387512922286987}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.8057556748390198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7543383836746216},{"id":"https://openalex.org/C26993612","wikidata":"https://www.wikidata.org/wiki/Q910154","display_name":"Panacea (medicine)","level":3,"score":0.7312921285629272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5532464981079102},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4993557929992676},{"id":"https://openalex.org/C2992317946","wikidata":"https://www.wikidata.org/wiki/Q712144","display_name":"De facto","level":2,"score":0.4683261215686798},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4595981240272522},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4368496239185333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43427902460098267},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.433262437582016},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4248199760913849},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22821998596191406},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09412992000579834},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09012371301651001},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07387512922286987},{"id":"https://openalex.org/C204787440","wikidata":"https://www.wikidata.org/wiki/Q188504","display_name":"Alternative medicine","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3529337.3529355","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3529337.3529355","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:infoscience.epfl.ch:302684","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/302684","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"WoS","raw_type":"research article"}],"best_oa_location":{"id":"pmh:oai:infoscience.epfl.ch:302684","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/302684","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"WoS","raw_type":"research article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W342285082","https://openalex.org/W1493691328","https://openalex.org/W1526669747","https://openalex.org/W1529533208","https://openalex.org/W1594946023","https://openalex.org/W1982287794","https://openalex.org/W2022166150","https://openalex.org/W2080133951","https://openalex.org/W2086895219","https://openalex.org/W2094728533","https://openalex.org/W2114538147","https://openalex.org/W2118100588","https://openalex.org/W2122843979","https://openalex.org/W2140190241","https://openalex.org/W2148019918","https://openalex.org/W2250911766","https://openalex.org/W2294774419","https://openalex.org/W2300469216","https://openalex.org/W2399361902","https://openalex.org/W2405686381","https://openalex.org/W2542998387","https://openalex.org/W2551361256","https://openalex.org/W2605089588","https://openalex.org/W2741750617","https://openalex.org/W2798649495","https://openalex.org/W2808284704","https://openalex.org/W2903963001","https://openalex.org/W2949700412","https://openalex.org/W2957191877","https://openalex.org/W2962916648","https://openalex.org/W2964263523","https://openalex.org/W2964855489","https://openalex.org/W2966349618","https://openalex.org/W2980525481","https://openalex.org/W3003496559","https://openalex.org/W3012000912","https://openalex.org/W3012715399","https://openalex.org/W3014705052","https://openalex.org/W3030216914","https://openalex.org/W3092962901","https://openalex.org/W3102952580","https://openalex.org/W3105114834","https://openalex.org/W3111851097","https://openalex.org/W3117053671","https://openalex.org/W3122741928","https://openalex.org/W3123375411","https://openalex.org/W3128650320","https://openalex.org/W3146701396","https://openalex.org/W3155399633","https://openalex.org/W3156859417","https://openalex.org/W4239749964","https://openalex.org/W4243470318","https://openalex.org/W4293159471"],"related_works":["https://openalex.org/W2073210364","https://openalex.org/W4242593755","https://openalex.org/W2123548032","https://openalex.org/W2564831469","https://openalex.org/W2002441522","https://openalex.org/W2100901739","https://openalex.org/W2073363395","https://openalex.org/W2889483553","https://openalex.org/W2034355831","https://openalex.org/W2314914385"],"abstract_inverted_index":{"Neural":[0],"methods":[1,30,48,67,130,151,196,230],"have":[2,31],"become":[3],"the":[4,8,41,69,79,83,99,110,126,139,147,186],"de-facto":[5],"choice":[6],"for":[7,87,109,174,200,241],"vast":[9],"majority":[10],"of":[11,40,103,128,154,163,178,194],"data":[12,202],"analysis":[13,39],"tasks,":[14],"and":[15,45,71,76,105,119,157,166,168,227],"entity":[16,100],"alignment":[17],"(EA)":[18],"is":[19,169],"no":[20],"exception.":[21],"Not":[22],"surprisingly,":[23,37],"more":[24],"than":[25],"50":[26],"different":[27],"neural":[28,44,72,89,104,150,195,229],"EA":[29,47,90,118],"been":[32,50],"published":[33],"since":[34],"2017.":[35],"However,":[36],"an":[38,58,191],"differences":[42],"between":[43,117],"non-neural":[46,106,141],"has":[49],"lacking.":[51],"We":[52,74],"bridge":[53],"this":[54],"gap":[55],"by":[56,123],"performing":[57],"in-depth":[59],"comparison":[60],"among":[61],"five":[62],"carefully":[63],"chosen":[64],"representative":[65,148],"state-of-the-art":[66,140,149],"from":[68,190],"pre-neural":[70],"era.":[73],"unravel,":[75],"consequently":[77],"mitigate,":[78],"inherent":[80],"deficiencies":[81],"in":[82,95,152,209,221],"experimental":[84],"setup":[85],"utilized":[86],"evaluating":[88],"methods.":[91,107],"To":[92],"ensure":[93],"fairness":[94],"evaluation,":[96],"we":[97,113,243],"homogenize":[98],"matching":[101],"modules":[102],"Additionally,":[108],"first":[111],"time,":[112],"draw":[114],"a":[115,160,175,198,219],"parallel":[116],"record":[120],"linkage":[121],"(RL)":[122],"empirically":[124],"showcasing":[125],"ability":[127],"RL":[129],"to":[131,172,232,236],"perform":[132],"EA.":[133,180],"Our":[134,181],"results":[135,208],"indicate":[136],"that":[137],"Paris,":[138],"method,":[142],"statistically":[143],"significantly":[144],"outperforms":[145],"all":[146,201],"terms":[153],"both":[155],"efficacy":[156],"efficiency":[158],"across":[159],"wide":[161],"variety":[162],"dataset":[164],"types":[165],"scenarios,":[167],"second":[170],"only":[171],"BERT-INT":[173],"specific":[176],"scenario":[177],"cross-lingual":[179],"findings":[182],"shed":[183],"light":[184],"on":[185,225],"potential":[187],"problems":[188],"resulting":[189],"impulsive":[192],"application":[193],"as":[197,218],"panacea":[199],"analytics":[203],"tasks.":[204],"Overall,":[205],"our":[206],"work":[207,224],"two":[210],"overarching":[211],"conclusions:":[212],"(1)":[213],"Paris":[214],"should":[215],"be":[216,233],"used":[217],"baseline":[220],"every":[222],"follow-up":[223],"EA,":[226],"(2)":[228],"need":[231],"positioned":[234],"better":[235],"showcase":[237],"their":[238],"true":[239],"potential,":[240],"which":[242],"provide":[244],"multiple":[245],"recommendations.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}
