{"id":"https://openalex.org/W3215736701","doi":"https://doi.org/10.1080/10618600.2021.2003204","title":"A Probit Tensor Factorization Model For Relational Learning","display_name":"A Probit Tensor Factorization Model For Relational Learning","publication_year":2021,"publication_date":"2021-11-18","ids":{"openalex":"https://openalex.org/W3215736701","doi":"https://doi.org/10.1080/10618600.2021.2003204","mag":"3215736701"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2021.2003204","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.2003204","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","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":"https://openalex.org/A5100346529","display_name":"Ye Liu","orcid":"https://orcid.org/0000-0001-7237-7382"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ye Liu","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089012283","display_name":"Rui Song","orcid":"https://orcid.org/0000-0003-1875-2115"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rui Song","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059203764","display_name":"Wenbin Lu","orcid":"https://orcid.org/0000-0002-7320-4755"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenbin Lu","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090455375","display_name":"Yanghua Xiao","orcid":"https://orcid.org/0000-0001-8403-9591"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanghua Xiao","raw_affiliation_strings":["Department of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089012283"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":0.1341,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38210399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"31","issue":"3","first_page":"846","last_page":"855"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5623811483383179},{"id":"https://openalex.org/keywords/ordered-probit","display_name":"Ordered probit","score":0.5043026208877563},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4723796844482422},{"id":"https://openalex.org/keywords/probit","display_name":"Probit","score":0.4506736099720001},{"id":"https://openalex.org/keywords/multinomial-probit","display_name":"Multinomial probit","score":0.4486631155014038},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.44717854261398315},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44008171558380127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4234340488910675},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4106639623641968},{"id":"https://openalex.org/keywords/probit-model","display_name":"Probit model","score":0.378913938999176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37245285511016846},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3348470628261566},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2061225175857544},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.19582286477088928},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15530353784561157},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.08121001720428467}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5623811483383179},{"id":"https://openalex.org/C70339092","wikidata":"https://www.wikidata.org/wiki/Q7100715","display_name":"Ordered probit","level":2,"score":0.5043026208877563},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4723796844482422},{"id":"https://openalex.org/C184314375","wikidata":"https://www.wikidata.org/wiki/Q3117995","display_name":"Probit","level":2,"score":0.4506736099720001},{"id":"https://openalex.org/C46704056","wikidata":"https://www.wikidata.org/wiki/Q17086346","display_name":"Multinomial probit","level":3,"score":0.4486631155014038},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.44717854261398315},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44008171558380127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4234340488910675},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4106639623641968},{"id":"https://openalex.org/C67257552","wikidata":"https://www.wikidata.org/wiki/Q635217","display_name":"Probit model","level":2,"score":0.378913938999176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37245285511016846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3348470628261566},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2061225175857544},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.19582286477088928},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15530353784561157},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.08121001720428467}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2021.2003204","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.2003204","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1476930183","display_name":null,"funder_award_id":"2113637","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G68352016","display_name":null,"funder_award_id":"DMS-1555244","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W68132019","https://openalex.org/W102708294","https://openalex.org/W188608978","https://openalex.org/W205829674","https://openalex.org/W301343586","https://openalex.org/W340632675","https://openalex.org/W614875374","https://openalex.org/W1512387364","https://openalex.org/W1536734652","https://openalex.org/W1827214880","https://openalex.org/W1888732573","https://openalex.org/W1910578190","https://openalex.org/W1963826206","https://openalex.org/W1967087957","https://openalex.org/W1972365594","https://openalex.org/W1974403130","https://openalex.org/W1995368504","https://openalex.org/W1996564243","https://openalex.org/W2008114373","https://openalex.org/W2022166150","https://openalex.org/W2023246751","https://openalex.org/W2035182720","https://openalex.org/W2037933327","https://openalex.org/W2039750798","https://openalex.org/W2040006565","https://openalex.org/W2049633694","https://openalex.org/W2075517844","https://openalex.org/W2087409009","https://openalex.org/W2089349245","https://openalex.org/W2097266862","https://openalex.org/W2101802482","https://openalex.org/W2102363952","https://openalex.org/W2103367708","https://openalex.org/W2112292531","https://openalex.org/W2115871101","https://openalex.org/W2119101292","https://openalex.org/W2125027602","https://openalex.org/W2127795553","https://openalex.org/W2138485466","https://openalex.org/W2146130798","https://openalex.org/W2149025117","https://openalex.org/W2154851992","https://openalex.org/W2168175751","https://openalex.org/W2170407643","https://openalex.org/W2172684358","https://openalex.org/W2184957013","https://openalex.org/W2249040818","https://openalex.org/W2258054274","https://openalex.org/W2283196293","https://openalex.org/W2359252770","https://openalex.org/W2393319904","https://openalex.org/W2419120792","https://openalex.org/W2433281745","https://openalex.org/W2555756618","https://openalex.org/W2567289819","https://openalex.org/W2595697910","https://openalex.org/W2604272474","https://openalex.org/W2724395316","https://openalex.org/W2770340794","https://openalex.org/W2772124661","https://openalex.org/W2774837955","https://openalex.org/W2796165036","https://openalex.org/W2807764928","https://openalex.org/W2885408128","https://openalex.org/W2913187981","https://openalex.org/W2924170235","https://openalex.org/W2962756421","https://openalex.org/W2963276152","https://openalex.org/W2963819344","https://openalex.org/W2999009778","https://openalex.org/W3099329193","https://openalex.org/W3099387504","https://openalex.org/W3101896416","https://openalex.org/W3102532968","https://openalex.org/W3102581530","https://openalex.org/W3104097132","https://openalex.org/W4232932184","https://openalex.org/W6941071698"],"related_works":["https://openalex.org/W1521763318","https://openalex.org/W3124990839","https://openalex.org/W3130477596","https://openalex.org/W2209414691","https://openalex.org/W2093711417","https://openalex.org/W2130048491","https://openalex.org/W3125842590","https://openalex.org/W2343740399","https://openalex.org/W4232966784","https://openalex.org/W1590848475"],"abstract_inverted_index":{"With":[0],"the":[1,17,25,44,85,92,101,129,133,142,159],"proliferation":[2],"of":[3,19,24,29,50,74,84,109,145],"knowledge":[4,45],"graphs,":[5],"modeling":[6],"data":[7],"with":[8,122],"complex":[9],"multi-relational":[10],"structure":[11],"has":[12,66],"gained":[13],"increasing":[14],"attention":[15],"in":[16,43,72,100,106,157],"area":[18],"statistical":[20,30],"relational":[21,31,146],"learning.":[22],"One":[23],"most":[26],"important":[27],"goals":[28],"learning":[32],"is":[33,90],"link":[34,59],"prediction,":[35,60],"that":[36,91],"is,":[37],"predicting":[38],"whether":[39],"certain":[40],"relations":[41,94,97],"exist":[42],"graph.":[46],"A":[47],"large":[48],"number":[49],"models":[51,89],"and":[52,77,95,162],"algorithms":[53],"have":[54],"been":[55],"proposed":[56,149],"to":[57,68],"perform":[58],"among":[61],"which":[62,104,125],"tensor":[63,87,119,135,151],"factorization":[64,88,120,136,152],"method":[65],"proven":[67],"achieve":[69],"state-of-the-art":[70],"performance":[71],"terms":[73],"computation":[75,130],"efficiency":[76,131],"prediction":[78,160],"accuracy.":[79],"However,":[80],"a":[81,107,117],"common":[82],"drawback":[83],"existing":[86],"missing":[93],"nonexisting":[96],"are":[98,169],"treated":[99],"same":[102],"way,":[103],"results":[105],"loss":[108],"information.":[110],"To":[111],"address":[112],"this":[113,167],"issue,":[114],"we":[115],"propose":[116],"binary":[118,143],"model":[121,137,154],"probit":[123,150],"link,":[124],"not":[126],"only":[127],"inherits":[128],"from":[132],"classic":[134],"but":[138],"also":[139],"accounts":[140],"for":[141,166],"nature":[144],"data.":[147],"Our":[148],"(PTF)":[153],"shows":[155],"advantages":[156],"both":[158],"accuracy":[161],"interpretability.":[163],"Supplementary":[164],"files":[165],"article":[168],"available":[170],"online.":[171]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
