{"id":"https://openalex.org/W4414241785","doi":"https://doi.org/10.14778/3750601.3750657","title":"Accelerating Tabular Inference: Training Data Generation with TENET","display_name":"Accelerating Tabular Inference: Training Data Generation with TENET","publication_year":2025,"publication_date":"2025-08-01","ids":{"openalex":"https://openalex.org/W4414241785","doi":"https://doi.org/10.14778/3750601.3750657"},"language":"en","primary_location":{"id":"doi:10.14778/3750601.3750657","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3750601.3750657","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://iris.unibas.it/bitstream/11563/204356/1/p5303-veltri.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059057476","display_name":"Enzo Veltri","orcid":"https://orcid.org/0000-0001-9947-8909"},"institutions":[{"id":"https://openalex.org/I20272500","display_name":"University of Basilicata","ror":"https://ror.org/03tc05689","country_code":"IT","type":"education","lineage":["https://openalex.org/I20272500"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Enzo Veltri","raw_affiliation_strings":["University of Basilicata, Potenza, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Basilicata, Potenza, Italy","institution_ids":["https://openalex.org/I20272500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042429991","display_name":"Donatello Santoro","orcid":"https://orcid.org/0000-0002-5651-8584"},"institutions":[{"id":"https://openalex.org/I20272500","display_name":"University of Basilicata","ror":"https://ror.org/03tc05689","country_code":"IT","type":"education","lineage":["https://openalex.org/I20272500"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Donatello Santoro","raw_affiliation_strings":["University of Basilicata, Potenza, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Basilicata, Potenza, Italy","institution_ids":["https://openalex.org/I20272500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093471806","display_name":"Jean-Flavien Bussotti","orcid":null},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jean-Flavien Bussotti","raw_affiliation_strings":["EURECOM, Biot, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EURECOM, Biot, France","institution_ids":["https://openalex.org/I1902872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011336242","display_name":"Paolo Papotti","orcid":"https://orcid.org/0000-0003-0651-4128"},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Paolo Papotti","raw_affiliation_strings":["EURECOM, Biot, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EURECOM, Biot, France","institution_ids":["https://openalex.org/I1902872"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11565926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"12","first_page":"5303","last_page":"5306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.46549999713897705,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.46549999713897705,"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/inference","display_name":"Inference","score":0.5723999738693237},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5600000023841858},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5425000190734863},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4968000054359436},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.4634000062942505},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.44609999656677246},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4433000087738037},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.37049999833106995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8557999730110168},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5723999738693237},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5600000023841858},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5425000190734863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5109999775886536},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4968000054359436},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.4634000062942505},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4433000087738037},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38359999656677246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3671000003814697},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.36399999260902405},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C2780977526","wikidata":"https://www.wikidata.org/wiki/Q42417149","display_name":"Data exploration","level":3,"score":0.3212999999523163},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3750601.3750657","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3750601.3750657","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:iris.unibas.it:11563/204356","is_oa":true,"landing_page_url":"https://hdl.handle.net/11563/204356","pdf_url":"https://iris.unibas.it/bitstream/11563/204356/1/p5303-veltri.pdf","source":{"id":"https://openalex.org/S4377196360","display_name":"CINECA IRIS Institutional Research Information System (University of Basilicata)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20272500","host_organization_name":"University of Basilicata","host_organization_lineage":["https://openalex.org/I20272500"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:iris.unibas.it:11563/204356","is_oa":true,"landing_page_url":"https://hdl.handle.net/11563/204356","pdf_url":"https://iris.unibas.it/bitstream/11563/204356/1/p5303-veltri.pdf","source":{"id":"https://openalex.org/S4377196360","display_name":"CINECA IRIS Institutional Research Information System (University of Basilicata)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20272500","host_organization_name":"University of Basilicata","host_organization_lineage":["https://openalex.org/I20272500"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414241785.pdf"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W3191286626","https://openalex.org/W4287778999","https://openalex.org/W4389609861"],"related_works":[],"abstract_inverted_index":{"Tabular":[0],"Natural":[1],"Language":[2],"Inference":[3],"(TNLI)":[4],"involves":[5],"machine":[6],"learning":[7],"models":[8,25,219],"that":[9,71,106],"assess":[10],"whether":[11],"structured":[12,192],"tabular":[13,180],"data":[14,73,122,133,138,193],"supports":[15],"or":[16,67],"contradicts":[17],"a":[18,45,84,94],"hypothesis":[19],"formulated":[20],"in":[21],"natural":[22,195],"language.":[23],"TNLI":[24,55,58,214],"typically":[26],"require":[27],"large":[28,95],"sets":[29],"of":[30,51,87,212],"training":[31,52,59,98],"examples,":[32],"which":[33],"are":[34,109,139,154],"costly":[35,64],"to":[36,91,146,159,176,217,223],"produce":[37],"manually.":[38],"In":[39,78],"this":[40],"demonstration,":[41],"we":[42],"present":[43],"Tenet,":[44],"system":[46],"for":[47,54,113,163],"the":[48,104,110,136,209],"automatic":[49],"generation":[50],"examples":[53,70,90,162],"applications.":[56],"Existing":[57],"approaches":[60],"either":[61],"depend":[62],"on":[63,103,131,226],"human":[65],"annotation":[66],"generate":[68,93],"simplistic":[69],"lack":[72],"diversity":[74],"and":[75,96,116,183,187],"complex":[76,117],"reasoning.":[77],"contrast,":[79],"Tenet":[80,100,190,207],"can":[81],"start":[82],"from":[83,128,179],"small":[85],"set":[86],"manually":[88,227],"annotated":[89,161],"automatically":[92],"diverse":[97,132],"dataset.":[99],"is":[101],"based":[102,130],"idea":[105],"SQL":[107,150],"queries":[108,142],"right":[111],"tool":[112],"obtaining":[114],"rich":[115],"generated":[118,185],"examples.":[119,229],"To":[120],"ensure":[121],"variety,":[123],"evidence-queries":[124],"extract":[125],"cell":[126],"values":[127],"tables":[129],"patterns.":[134],"Once":[135],"relevant":[137],"identified,":[140],"semantic":[141],"define":[143],"different":[144,201],"ways":[145],"interpret":[147],"it":[148],"using":[149],"clauses.":[151],"These":[152],"interpretations":[153],"then":[155],"verbalized":[156],"as":[157],"text":[158],"create":[160],"TNLI.":[164],"This":[165],"demonstration":[166],"offers":[167],"an":[168],"interactive":[169],"experience":[170],"where":[171],"users":[172,203],"will":[173,204],"be":[174],"able":[175],"select":[177],"evidence":[178],"data,":[181],"inspect":[182],"refine":[184],"queries,":[186],"observe":[188],"how":[189,206],"transforms":[191],"into":[194],"language":[196],"hypotheses.":[197],"By":[198],"engaging":[199],"with":[200,220],"scenarios,":[202],"see":[205],"enables":[208],"rapid":[210],"creation":[211],"high-quality":[213],"datasets,":[215],"leading":[216],"inference":[218],"performance":[221],"comparable":[222],"those":[224],"trained":[225],"crafted":[228]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
