{"id":"https://openalex.org/W7162107512","doi":"https://doi.org/10.48550/arxiv.2605.21974","title":"Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables","display_name":"Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162107512","doi":"https://doi.org/10.48550/arxiv.2605.21974"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21974","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.2605.21974","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035067496","display_name":"Jingxuan Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Jingxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136745434","display_name":"Zhiqiang Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136765502","display_name":"Yuxiang Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yuxiang","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":0.8133000135421753,"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":0.8133000135421753,"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":0.04430000111460686,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.02810000069439411,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/schema","display_name":"Schema (genetic algorithms)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/serialization","display_name":"Serialization","score":0.5368000268936157},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47200000286102295},{"id":"https://openalex.org/keywords/dependency-graph","display_name":"Dependency graph","score":0.41029998660087585},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.39879998564720154},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.38269999623298645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6597999930381775},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.5368000268936157},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47350001335144043},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44620001316070557},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.41029998660087585},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.28279998898506165},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.26159998774528503}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21974","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.2605.21974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21974","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":[{"id":"https://metadata.un.org/sdg/8","score":0.4222666025161743,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"An":[0],"extraction":[1,96],"schema":[2,32,72],"should":[3],"not":[4],"reduce":[5],"knowledge":[6],"graph":[7,163],"fidelity.":[8],"On":[9],"statistical":[10],"CSV,":[11],"however,":[12],"it":[13],"can.":[14],"We":[15,98],"study":[16],"country-by-year":[17],"time-series":[18],"matrices,":[19,186],"a":[20,71,75,110,147],"common":[21],"layout":[22],"on":[23,59,65,89,115],"open-data":[24],"portals.":[25],"In":[26],"this":[27,100],"setting,":[28],"serialization":[29],"format":[30,77],"and":[31,106,125,190],"constraints":[33],"interact":[34],"super-additively.":[35],"Their":[36],"joint":[37],"effect":[38],"exceeds":[39],"the":[40,86,129,133],"sum":[41],"of":[42],"independent":[43],"effects":[44],"by":[45],"up":[46,167],"to":[47,74,168],"+1.180":[48],"(2x2":[49],"factorial,":[50],"6":[51,196],"datasets).":[52],"Bootstrap":[53],"95%":[54],"CIs":[55],"are":[56],"strictly":[57],"positive":[58],"4/6":[60,90],"datasets,":[61,183],"with":[62],"strongest":[63],"evidence":[64],"wide":[66],"Type-II":[67,185],"matrices.":[68],"More":[69],"critically,":[70],"applied":[73],"mismatched":[76],"can":[78],"trigger":[79],"catastrophic":[80],"mismatch.":[81],"Fact":[82],"coverage":[83],"falls":[84],"below":[85],"unconstrained":[87],"baseline":[88],"datasets":[91],"through":[92],"entity":[93],"inflation":[94],"or":[95],"refusal.":[97],"call":[99],"observed":[101],"pattern":[102],"format-constraint":[103],"coupling.":[104],"Probing":[105],"token":[107],"ablation":[108],"support":[109,174],"surface-form":[111],"anchoring":[112],"explanation":[113],"centred":[114],"column-name":[116],"references.":[117],"Controlled":[118],"variants":[119],"across":[120,195],"format-schema":[121],"pairings,":[122],"GraphRAG":[123],"hosts,":[124],"LLM":[126,137],"families":[127],"show":[128],"same":[130],"direction":[131],"within":[132],"measured":[134],"scope;":[135],"one":[136],"family":[138],"shows":[139],"only":[140],"partial":[141],"activation.":[142],"The":[143],"observation":[144],"also":[145],"has":[146],"diagnostic":[148],"consequence.":[149],"Three":[150],"standard":[151],"retrieval":[152],"modes":[153],"largely":[154],"mask":[155],"construction":[156],"quality":[157],"(delta":[158],"&lt;=":[159],"1pp),":[160],"whereas":[161],"direct":[162],"access":[164],"exposes":[165],"gaps":[166],"+47.6pp":[169],"(p":[170],"&lt;":[171],"0.0001).":[172],"To":[173],"fidelity-aware":[175],"evaluation,":[176],"we":[177],"release":[178],"CSVFidelity-Bench.":[179],"It":[180],"contains":[181],"15":[182],"11":[184],"4":[187],"Type-III":[188],"tables,":[189],"1,892":[191],"Gold":[192],"Standard":[193],"facts":[194],"domains.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-23T00:00:00"}
