{"id":"https://openalex.org/W7161232789","doi":"https://doi.org/10.48550/arxiv.2605.14464","title":"From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics","display_name":"From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161232789","doi":"https://doi.org/10.48550/arxiv.2605.14464"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14464","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14464","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14464","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033048720","display_name":"Lingze Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Lingze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001683040","display_name":"Shaofeng Cai","orcid":"https://orcid.org/0000-0001-8605-076X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Shaofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062167285","display_name":"Changshuo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Changshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136211786","display_name":"Zhongle Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Zhongle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101635565","display_name":"Yuncheng Wu","orcid":"https://orcid.org/0000-0002-1436-3916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yuncheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136235949","display_name":"Beng Chin Ooi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ooi, Beng Chin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6692000031471252,"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":0.6692000031471252,"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":0.2085999995470047,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.03889999911189079,"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/tuple","display_name":"Tuple","score":0.769599974155426},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.625},{"id":"https://openalex.org/keywords/relational-model","display_name":"Relational model","score":0.5521000027656555},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.5461999773979187},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.47119998931884766},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4611000120639801},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4171999990940094},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.40639999508857727},{"id":"https://openalex.org/keywords/relational-database-management-system","display_name":"Relational database management system","score":0.3675000071525574}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8126999735832214},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.769599974155426},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.625},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.5521000027656555},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4097999930381775},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.40639999508857727},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4059999883174896},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37529999017715454},{"id":"https://openalex.org/C24394798","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database management system","level":3,"score":0.3675000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34040001034736633},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C99436015","wikidata":"https://www.wikidata.org/wiki/Q1722436","display_name":"Relational calculus","level":4,"score":0.30970001220703125},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C40077939","wikidata":"https://www.wikidata.org/wiki/Q2336004","display_name":"Semi-structured data","level":3,"score":0.2628999948501587},{"id":"https://openalex.org/C5968703","wikidata":"https://www.wikidata.org/wiki/Q267136","display_name":"Database model","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14464","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14464","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14464","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14464","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Relational":[0],"data":[1,30,95,205],"stored":[2],"in":[3,67,196],"RDBMS":[4],"is":[5,41],"foundational":[6],"to":[7,38,44,107,118,150,175],"many":[8],"real-world":[9,186],"applications":[10],"across":[11,148],"domains":[12],"such":[13],"as":[14,101],"e-commerce,":[15],"finance,":[16],"and":[17,49,70,103,141,171,178],"sociality.":[18],"While":[19],"deep":[20],"neural":[21],"networks":[22],"(DNNs)":[23],"have":[24],"achieved":[25],"strong":[26],"performance":[27],"on":[28,58,125,184],"tabular":[29],"with":[31,90],"a":[32,83,157,201],"single":[33],"table,":[34],"extending":[35],"these":[36,126],"models":[37],"relational":[39,94,163,187,204],"databases":[40,188],"challenging":[42],"due":[43],"the":[45,112],"normalized":[46],"multi-table":[47],"structure":[48,89],"complex":[50],"inter-table":[51],"relationships.":[52],"Existing":[53],"approaches":[54],"often":[55],"rely":[56],"strictly":[57],"schema-defined":[59],"graphs,":[60],"which":[61,134,143,165],"overlook":[62],"implicit":[63],"semantic":[64,120],"signals":[65],"embedded":[66],"tuple":[68,99],"attributes":[69,100],"suffer":[71],"from":[72],"rigid":[73],"connectivity.":[74,153],"In":[75],"this":[76],"work,":[77],"we":[78,128,155],"propose":[79,156],"Retrieval-Augmented":[80],"Modeling":[81],"(RAM),":[82],"novel":[84],"framework":[85],"that":[86,190],"combines":[87],"graph":[88,152,172],"attribute":[91,167],"semantics":[92],"for":[93,138,162,203],"analytics.":[96,206],"RAM":[97,191],"treats":[98],"tokens":[102],"uses":[104],"random":[105],"walks":[106],"construct":[108],"contextual":[109],"documents,":[110,127],"enabling":[111],"use":[113],"of":[114],"information":[115],"retrieval":[116],"techniques":[117],"estimate":[119],"relevance":[121,137],"between":[122],"tuples.":[123],"Building":[124],"introduce":[129],"two":[130],"retrieval-based":[131],"augmentations:":[132],"ATRA,":[133],"leverages":[135],"intra-table":[136],"contrastive":[139],"learning,":[140],"ETRA,":[142],"links":[144],"semantically":[145],"related":[146],"tuples":[147],"tables":[149],"enhance":[151],"Then,":[154],"layer-wise":[158],"model":[159],"architecture":[160],"tailored":[161],"data,":[164],"involves":[166],"embedding,":[168],"feature":[169],"integration,":[170],"aggregation":[173],"layers":[174],"enable":[176],"expressive":[177],"flexible":[179],"representation":[180],"learning.":[181],"Extensive":[182],"experiments":[183],"five":[185],"demonstrate":[189],"consistently":[192],"outperforms":[193],"existing":[194],"baselines":[195],"diverse":[197],"prediction":[198],"tasks,":[199],"establishing":[200],"state-of-the-art":[202]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}