{"id":"https://openalex.org/W4413066733","doi":"https://doi.org/10.1007/s10618-025-01128-0","title":"Combating confirmation bias: a unified pseudo-labeling framework for entity alignment","display_name":"Combating confirmation bias: a unified pseudo-labeling framework for entity alignment","publication_year":2025,"publication_date":"2025-07-18","ids":{"openalex":"https://openalex.org/W4413066733","doi":"https://doi.org/10.1007/s10618-025-01128-0"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-025-01128-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01128-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01128-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01128-0.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021437531","display_name":"Qijie Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Qijie Ding","raw_affiliation_strings":["Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014017635","display_name":"Jie Yin","orcid":"https://orcid.org/0000-0002-2063-8437"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jie Yin","raw_affiliation_strings":["Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-2063-8437","affiliations":[{"raw_affiliation_string":"Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438529","display_name":"Daokun Zhang","orcid":"https://orcid.org/0000-0002-1803-5768"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daokun Zhang","raw_affiliation_strings":["Department of Data Science and ArtificialIntelligence, Monash University, Melbourne, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-1803-5768","affiliations":[{"raw_affiliation_string":"Department of Data Science and ArtificialIntelligence, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015817857","display_name":"Junbin Gao","orcid":"https://orcid.org/0000-0001-9803-0256"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Junbin Gao","raw_affiliation_strings":["Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-9803-0256","affiliations":[{"raw_affiliation_string":"Discipline of Business Analytics, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014017635"],"corresponding_institution_ids":["https://openalex.org/I129604602"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":6.4293,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.9625343,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"39","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T11719","display_name":"Data Quality and Management","score":0.998199999332428,"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":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7834135890007019},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7404701113700867},{"id":"https://openalex.org/keywords/economic-shortage","display_name":"Economic shortage","score":0.5960193872451782},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5807428956031799},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5124252438545227},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.446696013212204},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.43662768602371216},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4131416082382202},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3805408775806427},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33578377962112427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3300284445285797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1383783221244812}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7834135890007019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404701113700867},{"id":"https://openalex.org/C194051981","wikidata":"https://www.wikidata.org/wiki/Q1337691","display_name":"Economic shortage","level":3,"score":0.5960193872451782},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5807428956031799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5124252438545227},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.446696013212204},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.43662768602371216},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4131416082382202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3805408775806427},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33578377962112427},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3300284445285797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1383783221244812},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10618-025-01128-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01128-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01128-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:monash.edu:publications/5e152c22-796e-46d4-a406-2efd24633b8f","is_oa":true,"landing_page_url":"https://research.monash.edu/en/publications/5e152c22-796e-46d4-a406-2efd24633b8f","pdf_url":"https://researchmgt.monash.edu/ws/files/741108796/725333181-oa.pdf","source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ding, Q, Yin, J, Zhang, D & Gao, J 2025, 'Combating confirmation bias : a unified pseudo-labeling framework for entity alignment', Data Mining and Knowledge Discovery, vol. 39, no. 5, 51. https://doi.org/10.1007/s10618-025-01128-0","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10618-025-01128-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01128-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01128-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.44999998807907104}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320966","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413066733.pdf","grobid_xml":"https://content.openalex.org/works/W4413066733.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W1597082186","https://openalex.org/W2022166150","https://openalex.org/W2080133951","https://openalex.org/W2094728533","https://openalex.org/W2123871098","https://openalex.org/W2250539671","https://openalex.org/W2283196293","https://openalex.org/W2300469216","https://openalex.org/W2551361256","https://openalex.org/W2741750617","https://openalex.org/W2808284704","https://openalex.org/W2889787757","https://openalex.org/W2890187992","https://openalex.org/W2895094948","https://openalex.org/W2955238243","https://openalex.org/W2962916648","https://openalex.org/W2963930099","https://openalex.org/W2964051675","https://openalex.org/W2966126249","https://openalex.org/W2970172244","https://openalex.org/W2970188762","https://openalex.org/W2971001654","https://openalex.org/W2980525481","https://openalex.org/W2990231018","https://openalex.org/W2998269939","https://openalex.org/W3001896264","https://openalex.org/W3012000912","https://openalex.org/W3030216914","https://openalex.org/W3035191547","https://openalex.org/W3091002423","https://openalex.org/W3091993229","https://openalex.org/W3098583774","https://openalex.org/W3111851097","https://openalex.org/W3122741928","https://openalex.org/W3174697615","https://openalex.org/W3180562345","https://openalex.org/W4206337156","https://openalex.org/W4221150350","https://openalex.org/W4224309032","https://openalex.org/W4233762729","https://openalex.org/W4283372881","https://openalex.org/W4308749892","https://openalex.org/W4320060459","https://openalex.org/W4385284171","https://openalex.org/W4385567259","https://openalex.org/W6836428340"],"related_works":["https://openalex.org/W2579473328","https://openalex.org/W2509147714","https://openalex.org/W4239898203","https://openalex.org/W2586833044","https://openalex.org/W2603666417","https://openalex.org/W46955353","https://openalex.org/W2315021981","https://openalex.org/W2955295882","https://openalex.org/W2145869671","https://openalex.org/W3098003361"],"abstract_inverted_index":{"Abstract":[0],"Entity":[1],"alignment":[2,99,214],"(EA)":[3],"aims":[4],"at":[5],"identifying":[6],"equivalent":[7],"entity":[8,70,98,133,158,255],"pairs":[9,71],"across":[10,164],"different":[11,38],"knowledge":[12,43],"graphs":[13],"(KGs)":[14],"that":[15,30,123,176],"refer":[16],"to":[17,40,66,76,128,156,172,204,208],"the":[18,32,42,51,77,84,130,238],"same":[19],"real-world":[20],"identity.":[21],"It":[22],"has":[23,92],"been":[24,93],"a":[25,108,250],"compelling":[26],"but":[27],"challenging":[28],"task":[29],"requires":[31],"integration":[33],"of":[34,53,87,132,218,240],"heterogeneous":[35],"information":[36],"from":[37],"KGs":[39],"expand":[41],"coverage":[44],"and":[45,160,228,234,246],"enhance":[46],"inference":[47],"abilities.":[48],"To":[49,101],"circumvent":[50],"shortage":[52],"prior":[54],"seed":[55,78,206],"alignments":[56,79,175,186,200,207],"provided":[57],"for":[58,80,117,213,254],"training,":[59],"recent":[60],"EA":[61],"models":[62,192],"utilize":[63],"pseudo-labeling":[64,91,126,147,222,252],"strategies":[65],"iteratively":[67],"add":[68],"unaligned":[69],"predicted":[72],"with":[73],"high":[74],"confidence":[75],"model":[81,211],"training.":[82],"However,":[83],"adverse":[85],"impact":[86],"confirmation":[88,104],"bias":[89],"during":[90],"largely":[94],"overlooked,":[95],"thus":[96],"hindering":[97],"performance.":[100],"systematically":[102],"combat":[103],"bias,":[105],"we":[106],"propose":[107],"new":[109],"U":[110],"nified":[111],"P":[112],"seudo-":[113],"L":[114],"abeling":[115],"framework":[116,253],"E":[118],"ntity":[119],"A":[120],"lignment":[121],"(UPL-EA)":[122],"explicitly":[124],"alleviates":[125],"errors":[127,223],"boost":[129],"performance":[131],"alignment.":[134,256],"UPL-EA":[135,219,241],"achieves":[136],"this":[137],"goal":[138],"through":[139],"two":[140,165],"key":[141],"innovations:":[142],"(1)":[143],"Optimal":[144],"Transport":[145],"(OT)-based":[146],"uses":[148],"discrete":[149],"OT":[150],"modeling":[151],"as":[152,249],"an":[153],"effective":[154,168],"means":[155],"determine":[157],"correspondences":[159],"reduce":[161],"erroneous":[162],"matches":[163],"KGs.":[166],"An":[167],"criterion":[169],"is":[170,224],"derived":[171],"infer":[173],"pseudo-labeled":[174,185,199],"satisfy":[177],"one-to-one":[178],"correspondences;":[179],"(2)":[180],"Parallel":[181],"pseudo-label":[182],"ensembling":[183],"refines":[184],"by":[187],"combining":[188],"predictions":[189],"over":[190,242],"multiple":[191],"independently":[193],"trained":[194],"in":[195,220],"parallel.":[196],"The":[197,216],"ensembled":[198],"are":[201],"thereafter":[202],"used":[203],"augment":[205],"reinforce":[209],"subsequent":[210],"training":[212],"inference.":[215],"effectiveness":[217],"eliminating":[221],"both":[225],"theoretically":[226],"supported":[227],"experimentally":[229],"validated.":[230],"Our":[231],"extensive":[232],"results":[233],"in-depth":[235],"analyses":[236],"demonstrate":[237],"superiority":[239],"15":[243],"competitive":[244],"baselines":[245],"its":[247],"utility":[248],"general":[251]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}