{"id":"https://openalex.org/W4288049640","doi":"https://doi.org/10.1145/3524383.3524429","title":"Knowledge Graph Completion Based on Multi-Relation Graph Attention Network","display_name":"Knowledge Graph Completion Based on Multi-Relation Graph Attention Network","publication_year":2022,"publication_date":"2022-02-26","ids":{"openalex":"https://openalex.org/W4288049640","doi":"https://doi.org/10.1145/3524383.3524429"},"language":"en","primary_location":{"id":"doi:10.1145/3524383.3524429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524383.3524429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data and Education","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/A5044535114","display_name":"Qian Wei","orcid":"https://orcid.org/0009-0004-1906-6664"},"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":"Qian Wei","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":1,"corresponding_author_ids":["https://openalex.org/A5044535114"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.3979,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66387309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"428","last_page":"432"},"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":0.9947999715805054,"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":0.9914000034332275,"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":0.6358206272125244},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5573868155479431},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5436228513717651},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4556380808353424},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.45368871092796326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2751086354255676},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22600209712982178}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6358206272125244},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5573868155479431},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5436228513717651},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4556380808353424},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.45368871092796326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2751086354255676},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22600209712982178}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3524383.3524429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524383.3524429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data and Education","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1533230146","https://openalex.org/W2016753842","https://openalex.org/W2127795553","https://openalex.org/W2283196293","https://openalex.org/W2600702321","https://openalex.org/W2604314403","https://openalex.org/W2607662938","https://openalex.org/W2728059831","https://openalex.org/W2909137510","https://openalex.org/W2914592219","https://openalex.org/W2944878684","https://openalex.org/W2950393809","https://openalex.org/W2951105272","https://openalex.org/W2984902757","https://openalex.org/W2996899616","https://openalex.org/W3103296573","https://openalex.org/W3129070541","https://openalex.org/W3200916032","https://openalex.org/W6672077861","https://openalex.org/W6718112784","https://openalex.org/W6755573351"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4234874385","https://openalex.org/W2390279801","https://openalex.org/W2323648130","https://openalex.org/W2358668433","https://openalex.org/W2157140558","https://openalex.org/W2376932109","https://openalex.org/W2378782423","https://openalex.org/W2001405890","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Knowledge":[0,83],"Graph":[1,84,89],"Completion":[2,85],"(KGC),":[3],"can":[4],"be":[5],"performed":[6],"mainly":[7],"by":[8],"inferring":[9],"missing":[10],"facts":[11],"from":[12],"entities":[13],"and":[14,52,110,128],"relations":[15,65],"already":[16],"in":[17,46,74],"the":[18,47,129,132],"knowledge":[19,71,96],"graphs.":[20,72],"However,":[21],"most":[22],"methods":[23],"for":[24,59,69,93,122],"KGC":[25],"only":[26],"focus":[27],"on":[28,87],"modeling":[29],"undirected":[30],"or":[31],"single":[32],"relational":[33],"graph":[34,97,103],"data,":[35],"ignoring":[36],"semantic":[37],"information":[38,45,68],"of":[39,134],"multiple":[40],"relations.":[41],"We":[42,99,124],"argue":[43],"that":[44],"directed":[48],"edge":[49],"flows":[50],"bi-directionally":[51],"there":[53],"exists":[54],"a":[55,79,101,118],"latent":[56,63],"reverse":[57,64,112],"relation":[58],"each":[60,108],"relation.":[61],"These":[62],"contain":[66],"additional":[67],"completing":[70],"Thus,":[73],"this":[75],"paper,":[76],"we":[77,116],"propose":[78,100],"novel":[80,119],"method":[81],"called":[82],"based":[86],"Multi-relation":[88],"Attention":[90],"neTwork":[91],"(KGC-MGAT)":[92],"more":[94],"accurate":[95],"completion.":[98],"multi-relational":[102],"attention":[104],"network":[105],"to":[106],"embed":[107],"triple":[109],"its":[111],"one":[113],"respectively.":[114],"Finally,":[115],"design":[117],"optimal":[120],"objective":[121],"training.":[123],"conduct":[125],"extensive":[126],"experiments,":[127],"results":[130],"show":[131],"superiority":[133],"our":[135],"method.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
