{"id":"https://openalex.org/W4366112272","doi":"https://doi.org/10.1109/lra.2023.3268044","title":"Employing Latent Categories of Entities for Knowledge Graph Embeddings With Contrastive Learning","display_name":"Employing Latent Categories of Entities for Knowledge Graph Embeddings With Contrastive Learning","publication_year":2023,"publication_date":"2023-04-17","ids":{"openalex":"https://openalex.org/W4366112272","doi":"https://doi.org/10.1109/lra.2023.3268044"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2023.3268044","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/lra.2023.3268044","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-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/A5056141893","display_name":"Jinfa Yang","orcid":"https://orcid.org/0000-0001-6138-7569"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinfa Yang","raw_affiliation_strings":["School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6138-7569","affiliations":[{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015981200","display_name":"Xianghua Ying","orcid":"https://orcid.org/0000-0002-9785-0727"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghua Ying","raw_affiliation_strings":["School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9785-0727","affiliations":[{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103205579","display_name":"Taiyan Chen","orcid":"https://orcid.org/0000-0001-9957-9144"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Taiyan Chen","raw_affiliation_strings":["School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9957-9144","affiliations":[{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056141893"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.1704,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52811117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"8","issue":"6","first_page":"3390","last_page":"3397"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987999796867371,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.989799976348877,"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":0.6741122603416443},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.672309398651123},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5983930826187134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5977565050125122},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5307934880256653},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5277776718139648},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49628907442092896},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4502895176410675},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.4167623519897461},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34100857377052307},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27520084381103516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18520241975784302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6741122603416443},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.672309398651123},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5983930826187134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5977565050125122},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5307934880256653},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5277776718139648},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49628907442092896},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4502895176410675},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.4167623519897461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34100857377052307},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27520084381103516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18520241975784302}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2023.3268044","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/lra.2023.3268044","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G4268905062","display_name":null,"funder_award_id":"61971008","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1533230146","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2127795553","https://openalex.org/W2138621090","https://openalex.org/W2187089797","https://openalex.org/W2226877337","https://openalex.org/W2250184916","https://openalex.org/W2283196293","https://openalex.org/W2432356473","https://openalex.org/W2728059831","https://openalex.org/W2772967090","https://openalex.org/W2783894900","https://openalex.org/W2798991696","https://openalex.org/W2942026896","https://openalex.org/W2949434543","https://openalex.org/W2963450615","https://openalex.org/W2967883831","https://openalex.org/W2997837621","https://openalex.org/W2997897037","https://openalex.org/W3005680577","https://openalex.org/W3033090297","https://openalex.org/W3096655658","https://openalex.org/W3102676774","https://openalex.org/W3128220336","https://openalex.org/W3156636935","https://openalex.org/W3164786495","https://openalex.org/W3166396011","https://openalex.org/W3173268697","https://openalex.org/W3173783447","https://openalex.org/W3205993174","https://openalex.org/W4226142803","https://openalex.org/W4283654626","https://openalex.org/W4283688275","https://openalex.org/W4285110218","https://openalex.org/W4287614078","https://openalex.org/W4293566037","https://openalex.org/W4295312788","https://openalex.org/W4319989255","https://openalex.org/W6608344535","https://openalex.org/W6631964550","https://openalex.org/W6678830454","https://openalex.org/W6718112784","https://openalex.org/W6746948564","https://openalex.org/W6747106673","https://openalex.org/W6751747387","https://openalex.org/W6758075616","https://openalex.org/W6761783306","https://openalex.org/W6766978945","https://openalex.org/W6774314701","https://openalex.org/W6784874100","https://openalex.org/W6785698184","https://openalex.org/W6791353385","https://openalex.org/W6850811617"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W4206028705","https://openalex.org/W2808284704","https://openalex.org/W2883748392","https://openalex.org/W2897702399","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W3200431764","https://openalex.org/W4288286922","https://openalex.org/W4206547516"],"abstract_inverted_index":{"Knowledge":[0],"graph":[1,31,100,141],"embedding":[2,32],"aims":[3],"to":[4,19,34,106],"represent":[5],"entities":[6,108,122],"and":[7,21,95,116,165],"relations":[8],"as":[9,40,75,83],"low-dimensional":[10],"vectors,":[11],"which":[12,102],"is":[13,27,145],"an":[14],"effective":[15],"way":[16],"for":[17,29,98,121],"robotics":[18],"learn":[20],"reason":[22],"about":[23],"semantic":[24,49],"knowledge.":[25],"It":[26,144],"crucial":[28],"knowledge":[30,99,140],"models":[33],"infer":[35],"various":[36],"relation":[37,62],"patterns,":[38],"such":[39,74,82,89],"symmetry/antisymmetry.":[41],"However,":[42],"most":[43],"existing":[44],"approaches":[45],"ignore":[46],"the":[47,111,118,124,131,162,171],"latent":[48,125],"categories":[50],"information":[51],"of":[52,133],"entities.":[53],"For":[54],"example,":[55],"when":[56],"robots":[57],"use":[58],"a":[59,93],"faucet":[60],"with":[61,123],"<italic":[63],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[64],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">OperatesOn</i>,":[65],"its":[66],"tail":[67],"entity":[68],"category":[69],"should":[70],"be":[71],"washable":[72],"objects,":[73,81],"apple,":[76],"bowl,":[77],"rather":[78],"than":[79],"non-washable":[80],"mobile":[84],"phone,":[85],"computer.":[86],"To":[87],"address":[88],"issues,":[90],"we":[91],"propose":[92],"novel":[94],"simple":[96],"framework":[97],"embedding,":[101],"utilizes":[103],"contrastive":[104],"learning":[105],"cluster":[107],"based":[109],"on":[110,137,161,170],"constraints":[112],"arising":[113],"from":[114],"relations,":[115],"enhances":[117],"discriminative":[119],"ability":[120],"same":[126],"category.":[127],"Experimental":[128],"results":[129],"demonstrate":[130],"effectiveness":[132],"our":[134,148],"proposed":[135],"method":[136,149],"four":[138],"standard":[139],"completion":[142],"benchmarks.":[143],"noteworthy":[146],"that":[147],"can":[150],"yield":[151],"some":[152],"new":[153],"state-of-the-art":[154],"results,":[155],"achieving":[156],"88.2%":[157],"MRR,":[158,167],"84.9%":[159],"Hits@1":[160,169],"AI2Thor":[163],"dataset,":[164],"39.8%":[166],"30.0%":[168],"FB15k-237":[172],"dataset.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}