{"id":"https://openalex.org/W4406158166","doi":"https://doi.org/10.1109/tbdata.2025.3527238","title":"Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph","display_name":"Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph","publication_year":2025,"publication_date":"2025-01-08","ids":{"openalex":"https://openalex.org/W4406158166","doi":"https://doi.org/10.1109/tbdata.2025.3527238"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2025.3527238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2025.3527238","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","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":null,"display_name":"Yao Liu","orcid":"https://orcid.org/0000-0003-0478-7819"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yao Liu","raw_affiliation_strings":["Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108064414","display_name":"Yongfei Zhang","orcid":"https://orcid.org/0000-0002-5080-1733"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfei Zhang","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","Pengcheng Laboratory, Shenzhen, China","School of Computer Science and Engineering, Beihang University, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00169207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"5","first_page":"2308","last_page":"2320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13062","display_name":"Cognitive Computing and Networks","score":0.8870999813079834,"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/T13062","display_name":"Cognitive Computing and Networks","score":0.8870999813079834,"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":0.8391293287277222},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.620910108089447},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5701887607574463},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5278522968292236},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5168731212615967},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.4534863829612732},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4359692931175232},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2902679443359375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2692229449748993}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8391293287277222},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.620910108089447},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5701887607574463},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5278522968292236},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5168731212615967},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.4534863829612732},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4359692931175232},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2902679443359375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2692229449748993}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2025.3527238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2025.3527238","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5946680137","display_name":null,"funder_award_id":"62472016","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8533821321","display_name":null,"funder_award_id":"62072022","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":25,"referenced_works":["https://openalex.org/W2022166150","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2499696929","https://openalex.org/W2604314403","https://openalex.org/W2604936503","https://openalex.org/W2755637027","https://openalex.org/W2774837955","https://openalex.org/W2912664727","https://openalex.org/W2914592219","https://openalex.org/W2963406064","https://openalex.org/W2963571857","https://openalex.org/W2977897270","https://openalex.org/W2981175810","https://openalex.org/W2997897037","https://openalex.org/W3004900685","https://openalex.org/W3030759655","https://openalex.org/W3087818399","https://openalex.org/W3120491054","https://openalex.org/W3212116457","https://openalex.org/W4307724846","https://openalex.org/W4360992654"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W2883748392","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"],"abstract_inverted_index":{"With":[0],"the":[1,71,91,97,138,147,152,161,167,180,187,195,199,202,210,226,232,244,251,256],"increasing":[2],"amount":[3],"of":[4,50,96,141,149,163,182,189,201,214,222,260],"data":[5,139,154,184,197],"in":[6,22,105,121,129],"various":[7],"domains,":[8],"knowledge":[9,32,57,123],"graphs":[10,33,48],"(KGs)":[11],"have":[12],"become":[13],"powerful":[14],"tools":[15],"for":[16,27,67,77,231],"representing":[17],"complex":[18],"and":[19,26,44,93,258],"heterogeneous":[20],"information":[21,30,95],"a":[23,115,142],"structured":[24],"way,":[25],"extracting":[28],"valuable":[29],"from":[31,124],"through":[34],"embedding":[35,63,86,102,191,212,229],"techniques":[36],"to":[37,74,100,135,170,185,225],"support":[38],"downstream":[39],"tasks":[40],"such":[41],"as":[42,56],"recommendation":[43],"Q&A":[45],"systems.":[46],"Knowledge":[47],"consist":[49],"triples":[51],"that":[52,194,209],"are":[53,65],"continuously":[54],"added":[55,183,196],"is":[58,81,127,217],"updated.":[59],"However,":[60],"most":[61],"existing":[62],"models":[64,87],"designed":[66],"static":[68],"graphs,":[69],"requiring":[70],"entire":[72],"model":[73,120,216,230,240],"be":[75],"retrained":[76],"each":[78,133],"update,":[79],"which":[80,122],"time-consuming.":[82],"Existing":[83],"global":[84],"dynamic":[85,107,190,211,228],"focus":[88,136],"on":[89,137,179,237],"exploiting":[90],"structural":[92],"relational":[94,116],"whole":[98],"graph":[99],"achieve":[101],"quality,":[103],"resulting":[104],"reduced":[106],"efficiency.":[108],"To":[109],"address":[110],"this":[111],"problem,":[112],"we":[113,173],"propose":[114],"clustering-based":[117],"parallel":[118,203],"space":[119],"different":[125,130],"domains":[126],"embedded":[128],"subspaces,":[131],"allowing":[132],"subspace":[134],"characteristics":[140,200],"specific":[143],"domain,":[144],"thereby":[145],"improving":[146,166],"quality":[148],"knowledge.":[150],"Second,":[151],"new":[153],"only":[155,242],"affects":[156],"some":[157],"subspaces":[158],"but":[159,248],"not":[160,241],"performance":[162],"other":[164],"spaces,":[165],"model's":[168],"adaptability":[169],"dynamics.":[171],"Furthermore,":[172],"employ":[174],"two":[175],"incremental":[176],"approaches":[177],"based":[178],"type":[181],"improve":[186],"efficiency":[188,213,245,259],"while":[192],"ensuring":[193],"preserves":[198],"space.":[204],"The":[205],"experimental":[206],"results":[207],"show":[208],"our":[215,239,261],"improved":[218],"by":[219,246,253],"an":[220],"average":[221],"50.3%":[223],"compared":[224],"SOTA":[227],"link":[233],"prediction":[234],"task.":[235],"Particularly":[236],"FB15K,":[238],"improves":[243],"41%":[247],"also":[249],"increases":[250],"accuracy":[252,257],"7.5%,":[254],"demonstrating":[255],"model.":[262]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}