{"id":"https://openalex.org/W3171160983","doi":"https://doi.org/10.1109/tnnls.2021.3083259","title":"Global Graph Attention Embedding Network for Relation Prediction in Knowledge Graphs","display_name":"Global Graph Attention Embedding Network for Relation Prediction in Knowledge Graphs","publication_year":2021,"publication_date":"2021-06-11","ids":{"openalex":"https://openalex.org/W3171160983","doi":"https://doi.org/10.1109/tnnls.2021.3083259","mag":"3171160983","pmid":"https://pubmed.ncbi.nlm.nih.gov/34115594"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3083259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3083259","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/A5100340637","display_name":"Qian Li","orcid":"https://orcid.org/0000-0002-9589-251X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Li","raw_affiliation_strings":["College of Computer Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-9589-251X","affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035378456","display_name":"Daling Wang","orcid":"https://orcid.org/0000-0003-1340-0778"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daling Wang","raw_affiliation_strings":["College of Computer Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057958949","display_name":"Shi Feng","orcid":"https://orcid.org/0000-0002-2846-7652"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shi Feng","raw_affiliation_strings":["College of Computer Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-2846-7652","affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111632482","display_name":"Cheng Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Niu","raw_affiliation_strings":["Tencent Company, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Company, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100386948","display_name":"Yifei Zhang","orcid":"https://orcid.org/0000-0003-4185-8663"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifei Zhang","raw_affiliation_strings":["College of Computer Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.8095,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.95740617,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"33","issue":"11","first_page":"6712","last_page":"6725"},"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":0.998199999332428,"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":0.9761999845504761,"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":0.6727981567382812},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6404929161071777},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6077197790145874},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.607634961605072},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5664886236190796},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.48176857829093933},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.42942914366722107},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33469605445861816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2690369188785553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6727981567382812},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6404929161071777},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6077197790145874},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.607634961605072},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5664886236190796},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.48176857829093933},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.42942914366722107},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33469605445861816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2690369188785553},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3083259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3083259","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:34115594","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34115594","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/G1954470819","display_name":null,"funder_award_id":"N2016008","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3278686258","display_name":null,"funder_award_id":"2018YFB1004700","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7187588322","display_name":"\u9762\u5411\u60c5\u611f\u4ea4\u4e92\u7684\u4eba\u673a\u5bf9\u8bdd\u6587\u672c\u751f\u6210\u6280\u672f\u7814\u7a76","funder_award_id":"61872074","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8807121210","display_name":null,"funder_award_id":"61772122","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"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":42,"referenced_works":["https://openalex.org/W1426956448","https://openalex.org/W1533712308","https://openalex.org/W2055629782","https://openalex.org/W2064675550","https://openalex.org/W2101420429","https://openalex.org/W2128407051","https://openalex.org/W2145769341","https://openalex.org/W2157331557","https://openalex.org/W2157529519","https://openalex.org/W2250342289","https://openalex.org/W2250911766","https://openalex.org/W2283196293","https://openalex.org/W2336631630","https://openalex.org/W2460319482","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2739716023","https://openalex.org/W2759136286","https://openalex.org/W2767287441","https://openalex.org/W2774837955","https://openalex.org/W2793488029","https://openalex.org/W2799103095","https://openalex.org/W2889344053","https://openalex.org/W2911286998","https://openalex.org/W2945955999","https://openalex.org/W2951105272","https://openalex.org/W2962886429","https://openalex.org/W2963217826","https://openalex.org/W2963380480","https://openalex.org/W2964072618","https://openalex.org/W2964605519","https://openalex.org/W2964926209","https://openalex.org/W2970572890","https://openalex.org/W2985882473","https://openalex.org/W3099387504","https://openalex.org/W3107569791","https://openalex.org/W3115476810","https://openalex.org/W6631964550","https://openalex.org/W6678830454","https://openalex.org/W6718112784","https://openalex.org/W6745779156","https://openalex.org/W6762867004"],"related_works":["https://openalex.org/W4234874385","https://openalex.org/W2604454537","https://openalex.org/W2808284704","https://openalex.org/W2897702399","https://openalex.org/W4206028705","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W2251363251","https://openalex.org/W4206547516","https://openalex.org/W4293236197"],"abstract_inverted_index":{"The":[0,280],"incompleteness":[1],"of":[2,29,37,71,78,91,100,270,289],"knowledge":[3,92,135,207,220],"graphs":[4,93],"triggers":[5],"considerable":[6],"research":[7],"interest":[8],"in":[9,97,160],"relation":[10,119,172,181,214,248],"prediction.":[11],"As":[12],"the":[13,27,44,54,57,61,79,85,98,140,155,161,193,210,224,253,260,268,287,293],"key":[14],"to":[15,25,144,153,176,191,251,274],"predicting":[16],"relations":[17,32,273],"among":[18],"entities,":[19],"many":[20],"efforts":[21],"have":[22],"been":[23],"devoted":[24],"learning":[26],"embeddings":[28,179,195,215,227,269],"entities":[30,244,271],"and":[31,49,94,102,129,170,180,200,213,216,245,266,272],"by":[33,121,196,223,230],"incorporating":[34],"a":[35,88,110,134,171,218,239,247],"variety":[36],"neighbors'":[38],"information":[39,45,77,124,159,261],"which":[40],"includes":[41],"not":[42],"only":[43],"from":[46,56,125,262],"direct":[47,127,198],"outgoing":[48,201],"incoming":[50,199],"neighbors":[51,59,128,202],"but":[52],"also":[53],"ones":[55],"indirect":[58],"on":[60,283],"multihop":[62,130,162],"paths.":[63,80],"However,":[64],"previous":[65],"models":[66],"usually":[67],"consider":[68],"entity":[69,167,178,185,194,212],"paths":[70,225],"limited":[72],"length":[73],"or":[74],"ignore":[75],"sequential":[76,158],"Either":[81],"simplification":[82],"will":[83],"make":[84],"model":[86,257,291],"lack":[87],"global":[89,112,123],"understanding":[90],"may":[95],"result":[96],"loss":[99],"important":[101],"indispensable":[103],"information.":[104,279],"In":[105],"this":[106],"article,":[107],"we":[108,137,237],"propose":[109,165],"novel":[111],"graph":[113,168,173,186,208,221,241,249],"attention":[114,169,174,187,250],"embedding":[115],"network":[116],"(GGAE)":[117],"for":[118,234],"prediction":[120],"combining":[122],"both":[126],"neighbors.":[131],"Concretely,":[132],"given":[133],"graph,":[136],"first":[138],"introduce":[139],"path":[141,150,231],"construction":[142],"algorithms":[143],"obtain":[145,177],"meaningful":[146],"paths,":[147,163],"then":[148],"design":[149],"modeling":[151,232],"methods":[152],"capture":[154,276],"potential":[156],"long-distance":[157],"final":[164],"an":[166,184,205],"mechanisms":[175],"embeddings.":[182],"Moreover,":[183],"mechanism":[188],"is":[189],"proposed":[190],"calculate":[192],"aggregating":[197],"from:":[203],"1)":[204],"original":[206,211],"with":[209,242],"2)":[217],"new":[219,240],"constructed":[222],"whose":[226],"are":[228],"updated":[229],"methods.":[233],"each":[235],"relation,":[236],"construct":[238],"related":[243],"present":[246],"learn":[252],"features.":[254],"Therefore,":[255],"our":[256,290],"can":[258],"encapsulate":[259],"different":[263],"distance":[264],"neighbors,":[265],"enable":[267],"better":[275],"all-sided":[277],"semantic":[278],"experimental":[281],"results":[282],"benchmark":[284],"datasets":[285],"verify":[286],"superiority":[288],"over":[292],"state-of-the-art":[294],"ones.":[295]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
