{"id":"https://openalex.org/W4388983831","doi":"https://doi.org/10.1109/taslp.2023.3336526","title":"A Novel Joint Training Model for Knowledge Base Question Answering","display_name":"A Novel Joint Training Model for Knowledge Base Question Answering","publication_year":2023,"publication_date":"2023-11-24","ids":{"openalex":"https://openalex.org/W4388983831","doi":"https://doi.org/10.1109/taslp.2023.3336526"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2023.3336526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2023.3336526","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"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/ACM Transactions on Audio, Speech, and Language Processing","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/A5030345736","display_name":"Shouhui Wang","orcid":"https://orcid.org/0000-0002-4258-1064"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shouhui Wang","raw_affiliation_strings":["School of Information, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102713872","display_name":"Biao Qin","orcid":"https://orcid.org/0000-0002-4304-675X"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Biao Qin","raw_affiliation_strings":["School of Information, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030345736"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":1.7397,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.87947951,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"32","issue":null,"first_page":"666","last_page":"679"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9984999895095825,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9945999979972839,"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":0.815973699092865},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7289714217185974},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6633484959602356},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.588853120803833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5731129050254822},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5725796818733215},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.515842616558075},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5145068168640137},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5080180168151855},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.4906734228134155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44291555881500244},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41684603691101074},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3909794092178345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35561317205429077},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33264610171318054}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.815973699092865},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7289714217185974},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6633484959602356},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.588853120803833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5731129050254822},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5725796818733215},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.515842616558075},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5145068168640137},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5080180168151855},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.4906734228134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44291555881500244},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41684603691101074},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3909794092178345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35561317205429077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33264610171318054},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2023.3336526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2023.3336526","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"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/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6350159928","display_name":null,"funder_award_id":"61772534","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/F4320322499","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":86,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W580074167","https://openalex.org/W1832693441","https://openalex.org/W1894439495","https://openalex.org/W2022166150","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2250225488","https://openalex.org/W2250342289","https://openalex.org/W2470673105","https://openalex.org/W2511149293","https://openalex.org/W2605089588","https://openalex.org/W2608787653","https://openalex.org/W2739716023","https://openalex.org/W2797383850","https://openalex.org/W2798416089","https://openalex.org/W2902551236","https://openalex.org/W2905122125","https://openalex.org/W2907356757","https://openalex.org/W2913623386","https://openalex.org/W2941424089","https://openalex.org/W2950985889","https://openalex.org/W2954097349","https://openalex.org/W2962739339","https://openalex.org/W2962745909","https://openalex.org/W2963077723","https://openalex.org/W2963490187","https://openalex.org/W2963655104","https://openalex.org/W2963682631","https://openalex.org/W2963761301","https://openalex.org/W2963840672","https://openalex.org/W2963854351","https://openalex.org/W2963897632","https://openalex.org/W2964082993","https://openalex.org/W2964120615","https://openalex.org/W2971155257","https://openalex.org/W2979000572","https://openalex.org/W2981039167","https://openalex.org/W2997937415","https://openalex.org/W2998272627","https://openalex.org/W3033893309","https://openalex.org/W3034273250","https://openalex.org/W3034862985","https://openalex.org/W3035407756","https://openalex.org/W3057365024","https://openalex.org/W3087931390","https://openalex.org/W3091091747","https://openalex.org/W3099023595","https://openalex.org/W3100058503","https://openalex.org/W3104748221","https://openalex.org/W3113844822","https://openalex.org/W3116847845","https://openalex.org/W3147216634","https://openalex.org/W3152740956","https://openalex.org/W3154271556","https://openalex.org/W3156128319","https://openalex.org/W3156366114","https://openalex.org/W3168997536","https://openalex.org/W3175795662","https://openalex.org/W3196690920","https://openalex.org/W3196758600","https://openalex.org/W3199047918","https://openalex.org/W3199258975","https://openalex.org/W3211959117","https://openalex.org/W3212129137","https://openalex.org/W3213414441","https://openalex.org/W3213855194","https://openalex.org/W4225533634","https://openalex.org/W4285254449","https://openalex.org/W4293042669","https://openalex.org/W4322718191","https://openalex.org/W6617055948","https://openalex.org/W6688533166","https://openalex.org/W6696085341","https://openalex.org/W6717114878","https://openalex.org/W6731370813","https://openalex.org/W6739901393","https://openalex.org/W6750378536","https://openalex.org/W6753030114","https://openalex.org/W6755207826","https://openalex.org/W6761673541","https://openalex.org/W6763701032","https://openalex.org/W6772039619","https://openalex.org/W6778883912","https://openalex.org/W6809976269","https://openalex.org/W6850625674"],"related_works":["https://openalex.org/W173870552","https://openalex.org/W2167202928","https://openalex.org/W3134247745","https://openalex.org/W4226243593","https://openalex.org/W3172691639","https://openalex.org/W4379379356","https://openalex.org/W2963582704","https://openalex.org/W3095980030","https://openalex.org/W4403761773","https://openalex.org/W3198510869"],"abstract_inverted_index":{"In":[0,60,103],"knowledge":[1,126],"base":[2],"question":[3],"answering":[4],"(KBQA)":[5],"systems,":[6],"relation":[7,19,52,84,98,137,174],"detection":[8,20,53,99,138,175],"and":[9,27,70,85,100,129,139,153,158,176,184],"entity":[10,58,86,101,140,182,185],"recognition":[11,141,183],"are":[12,146],"two":[13,67,108],"core":[14],"components.":[15],"However,":[16],"since":[17],"the":[18,38,107,125,130,159,169,179,192],"in":[21,40,49,195],"KBQA":[22,197],"contains":[23],"thousands":[24],"of":[25,76,173,181],"relations":[26,39],"this":[28],"task":[29,35],"always":[30],"becomes":[31],"a":[32,82,111,117,149,154],"zero-shot":[33],"learning":[34],"due":[36],"to":[37,96,105,135,168,191],"some":[41],"test":[42],"samples":[43],"while":[44],"they":[45],"have":[46],"not":[47,72],"appeared":[48],"training":[50],"data,":[51],"is":[54,121,166],"more":[55],"difficult":[56],"than":[57],"recognition.":[59,102],"addition,":[61,104],"previous":[62],"studies":[63],"only":[64],"considered":[65],"these":[66,188],"tasks":[68],"separately":[69],"did":[71],"take":[73],"full":[74],"advantage":[75],"their":[77],"correlation.":[78],"This":[79],"article":[80],"proposes":[81],"novel":[83],"joint":[87],"extraction":[88],"framework,":[89],"named":[90],"Gated-Attention-based":[91],"Joint":[92],"Training":[93],"Model":[94],"(Ga-JTM),":[95],"integrate":[97],"train":[106],"models":[109],"simultaneously,":[110],"knowledge-driven":[112],"gated":[113],"unit":[114],"based":[115],"on":[116,148],"multi-head":[118],"attention":[119],"mechanism":[120],"designed.":[122],"It":[123],"combines":[124],"graph":[127],"embeddings":[128],"current":[131],"context":[132],"semantic":[133],"information":[134],"process":[136],"tasks,":[142],"respectively.":[143],"The":[144],"experiments":[145],"conducted":[147],"single-relation":[150],"dataset":[151,156],"(SimpleQuestions)":[152],"multiple-relation":[155],"(WebQSP),":[157],"experimental":[160],"results":[161],"demonstrate":[162],"that":[163],"our":[164,196],"Ga-JTM":[165],"superior":[167],"state-of-the-art":[170],"(SOTA)":[171],"performance":[172,180,194],"can":[177],"improve":[178],"linking.":[186],"Finally,":[187],"improvements":[189],"contribute":[190],"SOTA":[193],"system.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}