{"id":"https://openalex.org/W4402980267","doi":"https://doi.org/10.1109/icme57554.2024.10687798","title":"MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models","display_name":"MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models","publication_year":2024,"publication_date":"2024-07-15","ids":{"openalex":"https://openalex.org/W4402980267","doi":"https://doi.org/10.1109/icme57554.2024.10687798"},"language":"en","primary_location":{"id":"doi:10.1109/icme57554.2024.10687798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme57554.2024.10687798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5006922042","display_name":"Jiabang He","orcid":"https://orcid.org/0009-0000-7452-7604"},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]},{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiabang He","raw_affiliation_strings":["Kuaishou Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kuaishou Technology,China","institution_ids":["https://openalex.org/I4210155967","https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050992792","display_name":"Jia Liu","orcid":"https://orcid.org/0000-0002-7441-3129"},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]},{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liu","raw_affiliation_strings":["Kuaishou Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kuaishou Technology,China","institution_ids":["https://openalex.org/I4210155967","https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436085","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0003-1228-6758"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["Singapore Management University,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore Management University,Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101819438","display_name":"Xiyao Li","orcid":"https://orcid.org/0000-0002-5767-5039"},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]},{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiyao Li","raw_affiliation_strings":["Kuaishou Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kuaishou Technology,China","institution_ids":["https://openalex.org/I4210155967","https://openalex.org/I4401726859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009074046","display_name":"Xing Xu","orcid":"https://orcid.org/0000-0001-5685-3123"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xu","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Computer Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Computer Science and Engineering,China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.6796,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.96170152,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9979000091552734,"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/T10028","display_name":"Topic Modeling","score":0.9979000091552734,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9819999933242798,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9769999980926514,"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":0.7312719225883484},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.6965182423591614},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4855368137359619},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4540856182575226},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44201427698135376},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.268924355506897}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312719225883484},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.6965182423591614},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4855368137359619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4540856182575226},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44201427698135376},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.268924355506897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme57554.2024.10687798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme57554.2024.10687798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"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":33,"referenced_works":["https://openalex.org/W1507806757","https://openalex.org/W1533230146","https://openalex.org/W2127795553","https://openalex.org/W2283196293","https://openalex.org/W2728059831","https://openalex.org/W2795389793","https://openalex.org/W2896457183","https://openalex.org/W2914592219","https://openalex.org/W2972167903","https://openalex.org/W3035524453","https://openalex.org/W3090114880","https://openalex.org/W3099700870","https://openalex.org/W3100859887","https://openalex.org/W3111984153","https://openalex.org/W3117339789","https://openalex.org/W3130909864","https://openalex.org/W3155001903","https://openalex.org/W3191973200","https://openalex.org/W3201503287","https://openalex.org/W4221021831","https://openalex.org/W4225632115","https://openalex.org/W4226142803","https://openalex.org/W4252707176","https://openalex.org/W4285261975","https://openalex.org/W4385270279","https://openalex.org/W4385572435","https://openalex.org/W6631964550","https://openalex.org/W6678830454","https://openalex.org/W6755207826","https://openalex.org/W6767905578","https://openalex.org/W6779977557","https://openalex.org/W6783961830","https://openalex.org/W6839682376"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2069592018","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Knowledge":[0],"Graph":[1],"Completion":[2],"(KGC)":[3],"aims":[4],"to":[5,49,74,93],"conduct":[6],"reasoning":[7],"on":[8,117,121],"the":[9,72,76,80],"facts":[10,34],"within":[11],"knowledge":[12,36,61],"graphs":[13,37],"and":[14,41,89,119],"automatically":[15],"infer":[16],"missing":[17],"links.":[18],"Existing":[19],"methods":[20,30,43],"can":[21],"mainly":[22],"be":[23],"categorized":[24],"into":[25],"structure-based":[26],"or":[27],"description-based.":[28],"Structure-based":[29],"effectively":[31],"represent":[32],"relational":[33],"in":[35,106],"using":[38],"entity":[39],"embeddings":[40],"description-based":[42],"leverage":[44],"pre-trained":[45,66],"language":[46,67],"models":[47,68],"(PLMs)":[48],"understand":[50],"textual":[51],"information.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"propose":[57],"Momentum":[58],"Contrast":[59],"for":[60],"graph":[62],"completion":[63],"with":[64,113],"Structure-Augmented":[65],"(MoCoSA),":[69],"which":[70],"allows":[71],"PLM":[73],"perceive":[75],"structural":[77],"information":[78],"by":[79],"adaptable":[81],"structure":[82],"encoder.":[83],"We":[84],"proposed":[85],"momentum":[86],"hard":[87],"negative":[88,91],"intra-relation":[90],"sampling":[92],"improve":[94],"learning":[95],"efficiency.":[96],"Experimental":[97],"results":[98],"demonstrate":[99],"that":[100],"our":[101],"approach":[102],"achieves":[103],"state-of-the-art":[104],"performance":[105],"terms":[107],"of":[108,115],"mean":[109],"reciprocal":[110],"rank":[111],"(MRR),":[112],"improvements":[114],"2.5%":[116],"WN18RR":[118],"21%":[120],"OpenBG500.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":4}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}