{"id":"https://openalex.org/W3129758539","doi":"https://doi.org/10.1109/tnnls.2021.3055147","title":"Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks","display_name":"Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks","publication_year":2021,"publication_date":"2021-02-20","ids":{"openalex":"https://openalex.org/W3129758539","doi":"https://doi.org/10.1109/tnnls.2021.3055147","mag":"3129758539","pmid":"https://pubmed.ncbi.nlm.nih.gov/33606639"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3055147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3055147","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100782897","display_name":"Zhifei Li","orcid":"https://orcid.org/0000-0003-0443-0094"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhifei Li","raw_affiliation_strings":["National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-0443-0094","affiliations":[{"raw_affiliation_string":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101820021","display_name":"Hai Liu","orcid":"https://orcid.org/0000-0003-3446-9301"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai Liu","raw_affiliation_strings":["National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-3446-9301","affiliations":[{"raw_affiliation_string":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020980410","display_name":"Zhaoli Zhang","orcid":"https://orcid.org/0000-0002-0844-0719"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoli Zhang","raw_affiliation_strings":["National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-0844-0719","affiliations":[{"raw_affiliation_string":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442033","display_name":"Tingting Liu","orcid":"https://orcid.org/0000-0002-9347-5974"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Liu","raw_affiliation_strings":["School of Education, Hubei University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-9347-5974","affiliations":[{"raw_affiliation_string":"School of Education, Hubei University, Wuhan, China","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103216531","display_name":"Naixue Xiong","orcid":"https://orcid.org/0000-0002-0394-4635"},"institutions":[{"id":"https://openalex.org/I192664086","display_name":"Northeastern State University","ror":"https://ror.org/01z7kzb45","country_code":"US","type":"education","lineage":["https://openalex.org/I192664086"]},{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Neal N. Xiong","raw_affiliation_strings":["National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China","Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]},{"raw_affiliation_string":"Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK, USA","institution_ids":["https://openalex.org/I192664086"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":34.5304,"has_fulltext":false,"cited_by_count":340,"citation_normalized_percentile":{"value":0.99813743,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"33","issue":"8","first_page":"3961","last_page":"3973"},"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":0.9865999817848206,"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":0.9513000249862671,"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/embedding","display_name":"Embedding","score":0.7909191846847534},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899008750915527},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5991336107254028},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5990138053894043},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5815978646278381},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.575884997844696},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5480279922485352},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5239053964614868},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5132381916046143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46290716528892517},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.45866313576698303},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3810938596725464},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3246240019798279}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7909191846847534},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899008750915527},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5991336107254028},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5990138053894043},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5815978646278381},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.575884997844696},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5480279922485352},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5239053964614868},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5132381916046143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46290716528892517},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.45866313576698303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3810938596725464},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3246240019798279},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3055147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3055147","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:33606639","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33606639","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2337525371","display_name":null,"funder_award_id":"2020YBZZ006","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3430789454","display_name":null,"funder_award_id":"62005092","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G400862627","display_name":null,"funder_award_id":"61875068","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4931275477","display_name":null,"funder_award_id":"6201101288","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5297414584","display_name":null,"funder_award_id":"CCNU20ZT017","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6969821397","display_name":null,"funder_award_id":"62077020","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7084389276","display_name":null,"funder_award_id":"CCNU2020ZN008","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7722758063","display_name":null,"funder_award_id":"61505064","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"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1426956448","https://openalex.org/W1533230146","https://openalex.org/W2022166150","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2095705004","https://openalex.org/W2127795553","https://openalex.org/W2183341477","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2604314403","https://openalex.org/W2624431344","https://openalex.org/W2728059831","https://openalex.org/W2759136286","https://openalex.org/W2766317792","https://openalex.org/W2774837955","https://openalex.org/W2786915849","https://openalex.org/W2808972931","https://openalex.org/W2888192920","https://openalex.org/W2899771611","https://openalex.org/W2900252255","https://openalex.org/W2907492528","https://openalex.org/W2908230750","https://openalex.org/W2909137510","https://openalex.org/W2910396842","https://openalex.org/W2912984848","https://openalex.org/W2914225118","https://openalex.org/W2916106175","https://openalex.org/W2920582597","https://openalex.org/W2923964967","https://openalex.org/W2945623882","https://openalex.org/W2949117887","https://openalex.org/W2950393809","https://openalex.org/W2951105272","https://openalex.org/W2962711740","https://openalex.org/W2963403868","https://openalex.org/W2963432357","https://openalex.org/W2963858333","https://openalex.org/W2963911286","https://openalex.org/W2963919031","https://openalex.org/W2964114465","https://openalex.org/W2964116313","https://openalex.org/W2964121744","https://openalex.org/W2964311892","https://openalex.org/W2972535098","https://openalex.org/W2976429401","https://openalex.org/W2995889583","https://openalex.org/W2997837621","https://openalex.org/W2997897037","https://openalex.org/W3099387504","https://openalex.org/W4210257598","https://openalex.org/W6631190155","https://openalex.org/W6631964550","https://openalex.org/W6637178625","https://openalex.org/W6674330103","https://openalex.org/W6678830454","https://openalex.org/W6718112784","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6739901393","https://openalex.org/W6745537798","https://openalex.org/W6748799445","https://openalex.org/W6752110883","https://openalex.org/W6754929296","https://openalex.org/W6758075616","https://openalex.org/W6759226055","https://openalex.org/W6763392795"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W2897702399","https://openalex.org/W4206028705","https://openalex.org/W2757431232","https://openalex.org/W2808284704","https://openalex.org/W2954554213","https://openalex.org/W2251363251","https://openalex.org/W4206547516","https://openalex.org/W4293236197","https://openalex.org/W2883748392"],"abstract_inverted_index":{"Knowledge":[0],"graph":[1,24,33,55],"(KG)":[2],"embedding":[3,8,122],"aims":[4],"to":[5,10,52,119,142],"study":[6],"the":[7,12,84,97,105,113,121,125],"representation":[9,25],"retain":[11],"inherent":[13],"structure":[14],"of":[15,41,47,61,87,99,150],"KGs.":[16],"Graph":[17],"neural":[18],"networks":[19],"(GNNs),":[20],"as":[21],"an":[22,38,88],"effective":[23],"technique,":[26],"have":[27,37],"shown":[28],"impressive":[29],"performance":[30,167],"in":[31],"learning":[32],"embedding.":[34],"However,":[35],"KGs":[36,164],"intrinsic":[39],"property":[40],"heterogeneity,":[42],"which":[43],"contains":[44],"various":[45,148],"types":[46,60,149],"entities":[48],"and":[49,57,153],"relations.":[50],"How":[51],"address":[53],"complex":[54],"data":[56],"aggregate":[58,155],"multiple":[59],"semantic":[62,135,151],"information":[63,152],"simultaneously":[64],"is":[65,81,102],"a":[66,72],"critical":[67],"issue.":[68],"In":[69],"this":[70],"article,":[71],"novel":[73],"heterogeneous":[74],"GNNs":[75],"framework":[76],"based":[77],"on":[78,161],"attention":[79],"mechanism":[80],"proposed.":[82],"Specifically,":[83],"neighbor":[85],"features":[86,111,132],"entity":[89,131],"are":[90,117],"first":[91],"aggregated":[92,118],"under":[93],"each":[94,109],"relation-path.":[95],"Then":[96],"importance":[98],"different":[100,134],"relation-paths":[101],"learned":[103,114],"through":[104],"relation":[106],"features.":[107,157],"Finally,":[108],"relation-path-based":[110],"with":[112,170],"weight":[115],"values":[116],"generate":[120],"representation.":[123],"Thus,":[124],"proposed":[126],"method":[127,145],"not":[128],"only":[129],"aggregates":[130],"from":[133],"aspects":[136],"but":[137],"also":[138],"allocates":[139],"appropriate":[140],"weights":[141],"them.":[143],"This":[144],"can":[146],"capture":[147],"selectively":[154],"informative":[156],"The":[158],"experiment":[159],"results":[160],"three":[162],"real-world":[163],"demonstrate":[165],"superior":[166],"when":[168],"compared":[169],"several":[171],"state-of-the-art":[172],"methods.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":63},{"year":2024,"cited_by_count":79},{"year":2023,"cited_by_count":83},{"year":2022,"cited_by_count":72},{"year":2021,"cited_by_count":19}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
