{"id":"https://openalex.org/W4224308031","doi":"https://doi.org/10.1145/3485447.3511922","title":"Time-aware Entity Alignment using Temporal Relational Attention","display_name":"Time-aware Entity Alignment using Temporal Relational Attention","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4224308031","doi":"https://doi.org/10.1145/3485447.3511922"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3511922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511922","pdf_url":null,"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":["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/A5022319543","display_name":"Chengjin Xu","orcid":"https://orcid.org/0000-0002-4942-9049"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Chengjin Xu","raw_affiliation_strings":["University of Bonn, Germany"],"affiliations":[{"raw_affiliation_string":"University of Bonn, Germany","institution_ids":["https://openalex.org/I135140700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084522939","display_name":"Fenglong Su","orcid":"https://orcid.org/0000-0002-7595-7515"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fenglong Su","raw_affiliation_strings":["National University of Defense Technology, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030297673","display_name":"Bo Xiong","orcid":"https://orcid.org/0000-0003-4089-8946"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bo Xiong","raw_affiliation_strings":["University of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"University of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067133778","display_name":"Jens Lehmann","orcid":"https://orcid.org/0000-0001-9108-4278"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jens Lehmann","raw_affiliation_strings":["University of Bonn, Germany and Fraunhofer IAIS, Germany"],"affiliations":[{"raw_affiliation_string":"University of Bonn, Germany and Fraunhofer IAIS, Germany","institution_ids":["https://openalex.org/I135140700"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022319543"],"corresponding_institution_ids":["https://openalex.org/I135140700"],"apc_list":null,"apc_paid":null,"fwci":2.8064,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.92146089,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"788","last_page":"797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T10028","display_name":"Topic Modeling","score":0.995199978351593,"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":0.984499990940094,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.9347885847091675},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7782689332962036},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6079598069190979},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5726646780967712},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5288280844688416},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5287827253341675},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.48880454897880554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44983112812042236},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.43592286109924316},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4220528304576874},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3783953785896301},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37244972586631775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2834815979003906}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.9347885847091675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7782689332962036},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6079598069190979},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5726646780967712},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5288280844688416},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5287827253341675},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.48880454897880554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44983112812042236},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.43592286109924316},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4220528304576874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3783953785896301},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37244972586631775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2834815979003906},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3485447.3511922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511922","pdf_url":null,"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:publica.fraunhofer.de:publica/430378","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/430378","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5199999809265137}],"awards":[{"id":"https://openalex.org/G6813789765","display_name":null,"funder_award_id":"81299,872592,952215,860801","funder_id":"https://openalex.org/F4320334322","funder_display_name":"HORIZON EUROPE Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W150699991","https://openalex.org/W398859631","https://openalex.org/W1552847225","https://openalex.org/W2022166150","https://openalex.org/W2551361256","https://openalex.org/W2610034660","https://openalex.org/W2741750617","https://openalex.org/W2808284704","https://openalex.org/W2889782235","https://openalex.org/W2890187992","https://openalex.org/W2903963001","https://openalex.org/W2949700412","https://openalex.org/W2962916648","https://openalex.org/W2963041663","https://openalex.org/W2963076818","https://openalex.org/W2963571857","https://openalex.org/W2964263523","https://openalex.org/W2964855489","https://openalex.org/W2998313947","https://openalex.org/W3001896264","https://openalex.org/W3012000912","https://openalex.org/W3030216914","https://openalex.org/W3034906292","https://openalex.org/W3075591326","https://openalex.org/W3098893606","https://openalex.org/W3101056714","https://openalex.org/W3102952580","https://openalex.org/W3122741928","https://openalex.org/W3146701396","https://openalex.org/W3155355417","https://openalex.org/W3155399633","https://openalex.org/W3156859417","https://openalex.org/W3167316272","https://openalex.org/W3211774129"],"related_works":["https://openalex.org/W2579899204","https://openalex.org/W2574209248","https://openalex.org/W2884490506","https://openalex.org/W2483088531","https://openalex.org/W4401920050","https://openalex.org/W2294686723","https://openalex.org/W2601871130","https://openalex.org/W2950359320","https://openalex.org/W4390493883","https://openalex.org/W2623500474"],"abstract_inverted_index":{"Knowledge":[0,24],"graph":[1,137],"(KG)":[2],"alignment":[3,54],"is":[4,12,88,118,141,153,171,181],"to":[5,14,90,143,155,183],"match":[6],"entities":[7,49],"in":[8],"different":[9],"KGs,":[10],"which":[11,87],"important":[13],"knowledge":[15],"fusion":[16],"and":[17,50,69,95,105,111,128,147,158,176,198],"integration.":[18],"Temporal":[19,81],"KGs":[20,42,57],"(TKGs)":[21],"extend":[22],"traditional":[23],"Graphs":[25],"(KGs)":[26],"by":[27,120],"associating":[28],"static":[29],"triples":[30],"with":[31,46],"specific":[32],"timestamps":[33,106,129],"(e.g.,":[34],"temporal":[35,149,197],"scopes":[36],"or":[37],"time":[38,159,179],"points).":[39],"Moreover,":[40],"open-world":[41],"(OKGs)":[43],"are":[44],"dynamic":[45],"new":[47,97],"emerging":[48,98],"timestamps.":[51,186],"While":[52],"entity":[53,117],"(EA)":[55],"between":[56,67,163],"has":[58],"drawn":[59],"increasing":[60],"attention":[61,151],"from":[62],"the":[63,112,122,209],"research":[64],"community,":[65],"EA":[66,200,211],"TKGs":[68],"OKGs":[70],"still":[71],"remains":[72],"unexplored.":[73],"In":[74],"this":[75],"work,":[76],"we":[77],"propose":[78],"a":[79,148,166,177],"novel":[80],"Relational":[82],"Entity":[83],"Alignment":[84],"method":[85,207],"(TREA)":[86],"able":[89],"learn":[91],"alignment-oriented":[92],"TKG":[93,191],"embeddings":[94,123],"represent":[96],"entities.":[99,135],"We":[100,187],"first":[101],"map":[102],"entities,":[103],"relations":[104,127],"into":[107],"an":[108],"embedding":[109],"space,":[110],"initial":[113],"feature":[114],"of":[115,124,161],"each":[116],"represented":[119],"fusing":[121],"its":[125,133],"connected":[126],"as":[130,132,193],"well":[131],"neighboring":[134],"A":[136],"neural":[138],"network":[139],"(GNN)":[140],"employed":[142],"capture":[144],"intra-graph":[145],"information":[146],"relational":[150],"mechanism":[152],"utilized":[154],"integrate":[156],"relation":[157],"features":[160],"links":[162],"nodes.":[164],"Finally,":[165],"margin-based":[167],"full":[168],"multi-class":[169],"log-loss":[170],"used":[172,182],"for":[173,195],"efficient":[174],"training":[175],"sequential":[178],"regularizer":[180],"model":[184],"unobserved":[185],"use":[188],"three":[189],"well-established":[190],"datasets,":[192],"references":[194],"evaluating":[196],"non-temporal":[199],"methods.":[201,212],"Experimental":[202],"results":[203],"show":[204],"that":[205],"our":[206],"outperforms":[208],"state-of-the-art":[210]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":14}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}