{"id":"https://openalex.org/W4224058934","doi":"https://doi.org/10.1145/3523150.3523172","title":"Knowledge Graph Entity Typing with Contrastive Learning","display_name":"Knowledge Graph Entity Typing with Contrastive Learning","publication_year":2022,"publication_date":"2022-01-15","ids":{"openalex":"https://openalex.org/W4224058934","doi":"https://doi.org/10.1145/3523150.3523172"},"language":"en","primary_location":{"id":"doi:10.1145/3523150.3523172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","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/A5087803823","display_name":"Guozhen Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guozhen Zhu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000577985","display_name":"Shunyu Yao","orcid":"https://orcid.org/0000-0002-1683-286X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunyu Yao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087803823"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.1327,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49775901,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"139","last_page":"144"},"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":0.9973000288009644,"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":0.9821000099182129,"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/computer-science","display_name":"Computer science","score":0.8224576711654663},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.657009482383728},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5881228446960449},{"id":"https://openalex.org/keywords/tying","display_name":"Tying","score":0.528933584690094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4941573441028595},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.43911588191986084},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.425290048122406},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.38370129466056824},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.21311047673225403},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20322006940841675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8224576711654663},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.657009482383728},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5881228446960449},{"id":"https://openalex.org/C2780938662","wikidata":"https://www.wikidata.org/wiki/Q973710","display_name":"Tying","level":2,"score":0.528933584690094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4941573441028595},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.43911588191986084},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.425290048122406},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.38370129466056824},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.21311047673225403},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20322006940841675},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3523150.3523172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1699691160","https://openalex.org/W2042792636","https://openalex.org/W2154851992","https://openalex.org/W2551361256","https://openalex.org/W2561529111","https://openalex.org/W2592329103","https://openalex.org/W2597507805","https://openalex.org/W2767287441","https://openalex.org/W2911730772","https://openalex.org/W2926245352","https://openalex.org/W2949972983","https://openalex.org/W2950393809","https://openalex.org/W2962756421","https://openalex.org/W2962756504","https://openalex.org/W2963149098","https://openalex.org/W2981612821","https://openalex.org/W3035559300","https://openalex.org/W3104097132"],"related_works":["https://openalex.org/W2998037107","https://openalex.org/W1597439699","https://openalex.org/W1569871744","https://openalex.org/W2016024526","https://openalex.org/W2379437105","https://openalex.org/W3139928442","https://openalex.org/W1567336638","https://openalex.org/W954011496","https://openalex.org/W2485077425","https://openalex.org/W4390279576"],"abstract_inverted_index":{"Knowledge":[0],"graph":[1],"entity":[2,30,38,43,70,87,102,112,139,151,162],"typing":[3,163],"is":[4,129],"an":[5],"important":[6],"way":[7],"to":[8],"complete":[9],"knowledge":[10],"graphs":[11],"(KGs),":[12],"aims":[13],"at":[14],"predicting":[15],"the":[16,58,68,93,97,100,108,124,135,138,142,147,150],"associating":[17],"types":[18,44],"of":[19,123,137,149],"certain":[20],"given":[21],"entities.":[22,51],"However,":[23],"previous":[24],"methods":[25,54],"suppose":[26],"that":[27,45,130],"many":[28],"(entity,":[29],"type)":[31],"pairs":[32],"can":[33,90,105,132],"be":[34],"obtained":[35],"for":[36,85],"each":[37],"type,":[39],"performing":[40],"poorly":[41],"on":[42,160],"only":[46],"have":[47],"a":[48,77],"few":[49],"associative":[50],"Besides,":[52],"these":[53],"cannot":[55],"fully":[56,106],"exploit":[57],"inherent":[59],"correlation":[60],"and":[61,104],"complementarity":[62],"information":[63,110],"across":[64],"different":[65],"entities":[66,98],"sharing":[67],"same":[69,101,143],"type.":[71],"To":[72],"this":[73],"end,":[74],"we":[75],"propose":[76],"novel":[78],"model":[79,156],"named":[80],"Contrastive":[81],"Entity":[82],"Typing":[83],"(CET)":[84],"KG":[86,161],"tying.":[88],"CET":[89],"better":[91],"learn":[92],"mutual":[94],"interactions":[95],"among":[96],"with":[99,141],"type":[103,113,144,152],"utilize":[107],"hierarchical":[109],"in":[111],"trees":[114],"by":[115],"two":[116],"contrastive":[117,126],"learning":[118,127],"modules.":[119],"The":[120],"main":[121],"benefit":[122],"proposed":[125],"modules":[128],"they":[131],"effectively":[133],"encourage":[134],"consistency":[136],"representations":[140],"while":[145],"improving":[146],"discriminability":[148],"classifiers.":[153],"Empirically,":[154],"our":[155],"achieves":[157],"state-of-the-art":[158],"results":[159],"benchmarks.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}