{"id":"https://openalex.org/W4224309032","doi":"https://doi.org/10.1145/3485447.3511945","title":"SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs","display_name":"SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4224309032","doi":"https://doi.org/10.1145/3485447.3511945"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3511945","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3511945","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3511945","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3511945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100441094","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-9226-4569"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040266310","display_name":"Haoyun Hong","orcid":"https://orcid.org/0009-0005-8858-4833"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyun Hong","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100737672","display_name":"Xinghao Wang","orcid":"https://orcid.org/0000-0001-6205-4889"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghao Wang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030612674","display_name":"Zeyi Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyi Chen","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025295947","display_name":"Evgeny Kharlamov","orcid":"https://orcid.org/0000-0003-3247-4166"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Evgeny Kharlamov","raw_affiliation_strings":["Bosch Center for AI, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bosch Center for AI, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052284218","display_name":"Yuxiao Dong","orcid":"https://orcid.org/0000-0002-6092-2002"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiao Dong","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044791875","display_name":"Jie Tang","orcid":"https://orcid.org/0000-0003-3487-4593"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.2671,"has_fulltext":true,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97847866,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"860","last_page":"870"},"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":0.9991000294685364,"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":0.9962999820709229,"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/computer-science","display_name":"Computer science","score":0.8262863159179688},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6630786061286926},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6419422626495361},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5393301248550415},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5342319011688232},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.532959520816803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5061869025230408},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4386492371559143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43559902906417847},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4196467995643616},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35450685024261475},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09523066878318787},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08299711346626282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8262863159179688},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6630786061286926},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6419422626495361},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5393301248550415},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5342319011688232},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.532959520816803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5061869025230408},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4386492371559143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43559902906417847},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4196467995643616},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35450685024261475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09523066878318787},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08299711346626282},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3485447.3511945","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3511945","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3511945","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2203.01044","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.01044","pdf_url":"https://arxiv.org/pdf/2203.01044","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3485447.3511945","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3511945","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3511945","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1040948671","display_name":null,"funder_award_id":"61836013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5614798656","display_name":null,"funder_award_id":"61825602","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224309032.pdf","grobid_xml":"https://content.openalex.org/works/W4224309032.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1990578345","https://openalex.org/W2024523516","https://openalex.org/W2058036501","https://openalex.org/W2114538147","https://openalex.org/W2551361256","https://openalex.org/W2551710909","https://openalex.org/W2741750617","https://openalex.org/W2808284704","https://openalex.org/W2889787757","https://openalex.org/W2890187992","https://openalex.org/W2896831016","https://openalex.org/W2903963001","https://openalex.org/W2949700412","https://openalex.org/W2952205826","https://openalex.org/W2953054275","https://openalex.org/W2962916648","https://openalex.org/W2964855489","https://openalex.org/W2966055354","https://openalex.org/W2971001654","https://openalex.org/W2980525481","https://openalex.org/W2997062749","https://openalex.org/W3027879771","https://openalex.org/W3030216914","https://openalex.org/W3032567415","https://openalex.org/W3035191547","https://openalex.org/W3036446966","https://openalex.org/W3099152386","https://openalex.org/W3111851097","https://openalex.org/W3156850293","https://openalex.org/W6648317951","https://openalex.org/W6797393643"],"related_works":["https://openalex.org/W3170999895","https://openalex.org/W4389374014","https://openalex.org/W4312414840","https://openalex.org/W4287117940","https://openalex.org/W34092691","https://openalex.org/W2131513867","https://openalex.org/W4389989304","https://openalex.org/W4300902524","https://openalex.org/W2036486025","https://openalex.org/W2952937263"],"abstract_inverted_index":{"Entity":[0],"alignment,":[1],"aiming":[2],"to":[3,48,68,132],"identify":[4],"equivalent":[5],"entities":[6,76,138],"across":[7],"different":[8],"knowledge":[9],"graphs":[10],"(KGs),":[11],"is":[12,66],"a":[13],"fundamental":[14],"problem":[15],"for":[16,33,56,123,136,173],"constructing":[17],"Web-scale":[18],"KGs.":[19,177],"Over":[20],"the":[21,26,38,46,60,70,74,88,119],"course":[22],"of":[23,41,54,72,90,164],"its":[24],"development,":[25],"label":[27,61,140],"supervision":[28,55,151],"has":[29],"been":[30],"considered":[31],"necessary":[32],"accurate":[34],"alignments.":[35],"Inspired":[36],"by":[37],"recent":[39],"progress":[40],"self-supervised":[42,120,168],"learning,":[43],"we":[44,50,117],"explore":[45],"extent":[47],"which":[49],"can":[51,93,152],"get":[52],"rid":[53],"entity":[57,64,91,124,174],"alignment.":[58,125],"Commonly,":[59],"information":[62],"(positive":[63],"pairs)":[65],"used":[67],"supervise":[69],"process":[71],"pulling":[73,108],"aligned":[75],"in":[77,176],"each":[78,105],"positive":[79,110],"pair":[80],"closer.":[81],"However,":[82],"our":[83],"theoretical":[84],"analysis":[85],"suggests":[86,166],"that":[87,148,167],"learning":[89,121,169],"alignment":[92,175],"actually":[94],"benefit":[95],"more":[96],"from":[97,104],"pushing":[98],"unlabeled":[99],"negative":[100],"pairs":[101,111],"far":[102],"away":[103],"other":[106],"than":[107],"labeled":[109],"close.":[112],"By":[113],"leveraging":[114],"this":[115,134],"discovery,":[116],"develop":[118],"objective":[122,135],"We":[126],"present":[127],"SelfKG":[128,149,165],"with":[129,158],"efficient":[130],"strategies":[131],"optimize":[133],"aligning":[137],"without":[139,150],"supervision.":[141],"Extensive":[142],"experiments":[143],"on":[144],"benchmark":[145],"datasets":[146],"demonstrate":[147],"match":[153],"or":[154],"achieve":[155],"comparable":[156],"results":[157],"state-of-the-art":[159],"supervised":[160],"baselines.":[161],"The":[162,178],"performance":[163],"offers":[170],"great":[171],"potential":[172],"code":[179],"and":[180],"data":[181],"are":[182],"available":[183],"at":[184],"https://github.com/THUDM/SelfKG.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
