{"id":"https://openalex.org/W2963220343","doi":"https://doi.org/10.1109/icpr.2018.8545027","title":"Knowledge Graph Embedding with Multiple Relation Projections","display_name":"Knowledge Graph Embedding with Multiple Relation Projections","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2963220343","doi":"https://doi.org/10.1109/icpr.2018.8545027","mag":"2963220343"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","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/A5001806269","display_name":"Kien Do","orcid":"https://orcid.org/0000-0002-0119-122X"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Kien Do","raw_affiliation_strings":["Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085471517","display_name":"Truyen Tran","orcid":"https://orcid.org/0000-0001-6531-8907"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Truyen Tran","raw_affiliation_strings":["Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045540854","display_name":"Svetha Venkatesh","orcid":"https://orcid.org/0000-0001-8675-6631"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Svetha Venkatesh","raw_affiliation_strings":["Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre of Pattern Recognition and Data Analysis, Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001806269"],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":null,"apc_paid":null,"fwci":3.17699934,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.92225944,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"332","last_page":"337"},"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":0.9937999844551086,"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":0.9883000254631042,"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/embedding","display_name":"Embedding","score":0.668835461139679},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6537384986877441},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.597030758857727},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.56479811668396},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5501199960708618},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5495402812957764},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5411007404327393},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5203472971916199},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.49283576011657715},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.483049213886261},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.4774813652038574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4094271659851074},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34829288721084595},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30120545625686646},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26692140102386475}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.668835461139679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6537384986877441},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.597030758857727},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.56479811668396},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5501199960708618},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5495402812957764},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5411007404327393},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5203472971916199},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.49283576011657715},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.483049213886261},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.4774813652038574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4094271659851074},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34829288721084595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30120545625686646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26692140102386475},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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":43,"referenced_works":["https://openalex.org/W68132019","https://openalex.org/W102708294","https://openalex.org/W205829674","https://openalex.org/W580074167","https://openalex.org/W1522301498","https://openalex.org/W1533230146","https://openalex.org/W2022166150","https://openalex.org/W2073587810","https://openalex.org/W2094728533","https://openalex.org/W2095739681","https://openalex.org/W2115662644","https://openalex.org/W2127795553","https://openalex.org/W2145544171","https://openalex.org/W2184957013","https://openalex.org/W2187089797","https://openalex.org/W2247119764","https://openalex.org/W2250184916","https://openalex.org/W2283196293","https://openalex.org/W2432356473","https://openalex.org/W2460423734","https://openalex.org/W2463781041","https://openalex.org/W2476140796","https://openalex.org/W2581887665","https://openalex.org/W2609678640","https://openalex.org/W2733421109","https://openalex.org/W2734806099","https://openalex.org/W2949972983","https://openalex.org/W2963432357","https://openalex.org/W2963448850","https://openalex.org/W2964059756","https://openalex.org/W2964116313","https://openalex.org/W2964121744","https://openalex.org/W3101896416","https://openalex.org/W4300427819","https://openalex.org/W6608344535","https://openalex.org/W6677185430","https://openalex.org/W6678830454","https://openalex.org/W6681270447","https://openalex.org/W6683461794","https://openalex.org/W6686133869","https://openalex.org/W6695596964","https://openalex.org/W6718112784","https://openalex.org/W6740645813"],"related_works":["https://openalex.org/W1995844989","https://openalex.org/W2037681520","https://openalex.org/W2794358477","https://openalex.org/W3181676408","https://openalex.org/W1549959306","https://openalex.org/W320292658","https://openalex.org/W2186138942","https://openalex.org/W2806326686","https://openalex.org/W2001007279","https://openalex.org/W2079674650"],"abstract_inverted_index":{"Knowledge":[0],"graphs":[1,26,89],"contain":[2],"rich":[3],"relational":[4],"structures":[5],"of":[6,33,57,92,97,117,126,151],"the":[7,34,60,77,115,123,139,186,195],"world,":[8],"and":[9,43,81,100,143,159,175],"thus":[10,82],"complement":[11],"data-driven":[12],"knowledge":[13,25,88],"discovery":[14],"from":[15],"heterogeneous":[16],"data.":[17],"Relational":[18],"inference":[19],"between":[20,54,79],"distant":[21],"entities":[22,44,58],"in":[23,59,95],"large-scale":[24],"demands":[27],"fast":[28],"relation-specific":[29],"algebraic":[30],"manipulations.":[31],"One":[32],"most":[35],"effective":[36],"methods":[37,67],"is":[38,133,144],"to":[39,86],"embed":[40],"symbolic":[41],"relations":[42,49],"into":[45],"continuous":[46],"spaces,":[47],"where":[48],"are":[50,83],"approximately":[51],"linear":[52],"translation":[53],"projected":[55],"images":[56],"relation":[61,65,118],"space.":[62],"However,":[63],"state-of-art":[64],"projection":[66,119,127,141],"such":[68],"as":[69],"TransR,":[70],"TransD":[71],"or":[72],"TransSparse":[73],"do":[74],"not":[75,84],"model":[76,167],"correlation":[78],"relations,":[80,93],"scalable":[85],"complex":[87],"with":[90,191],"thousands":[91],"both":[94],"term":[96],"computational":[98],"demand":[99],"statistical":[101],"robustness.":[102],"To":[103],"this":[104],"end":[105],"we":[106],"introduce":[107],"TransF,":[108],"a":[109,130,148,172],"novel":[110],"translation-based":[111,198],"method":[112],"which":[113],"mitigates":[114],"burden":[116],"by":[120,171,182],"explicitly":[121],"modeling":[122],"basis":[124],"subspaces":[125],"matrices.":[128],"As":[129],"result,":[131],"TransF":[132,180],"far":[134],"more":[135],"light":[136],"weight":[137],"than":[138],"existing":[140],"methods,":[142],"robust":[145],"when":[146],"facing":[147],"high":[149],"number":[150],"relations.":[152],"Experimental":[153],"results":[154],"on":[155,185],"canonical":[156],"link":[157],"prediction":[158,189],"triples":[160],"classification":[161],"tasks":[162],"show":[163],"that":[164],"our":[165],"proposed":[166],"outperforms":[168],"competing":[169],"rivals":[170],"large":[173],"margin":[174],"achieves":[176],"state-of-the-art":[177],"performance.":[178],"Especially,":[179],"improves":[181],"9%":[183],"(5%)":[184],"head/tail":[187],"entity":[188],"task":[190],"N-to-l":[192],"(l-to-N)":[193],"over":[194],"best":[196],"performing":[197],"method.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}