{"id":"https://openalex.org/W7167029812","doi":"https://doi.org/10.48550/arxiv.2607.00008","title":"SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction","display_name":"SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7167029812","doi":"https://doi.org/10.48550/arxiv.2607.00008"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00008","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00008","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.2607.00008","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139818708","display_name":"Sin Yu Bonnie Ho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ho, Sin Yu Bonnie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021661787","display_name":"Arlie Coles","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Coles, Arlie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139828032","display_name":"Erik Larsson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larsson, Erik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139742079","display_name":"Eric Marshall","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marshall, Eric","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139588401","display_name":"Nathan Bodenstab","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bodenstab, Nathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139895586","display_name":"Paul Vozila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vozila, Paul","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/T10028","display_name":"Topic Modeling","score":0.29339998960494995,"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":0.29339998960494995,"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":0.14010000228881836,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.10520000010728836,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7861999869346619},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.618399977684021},{"id":"https://openalex.org/keywords/schema-matching","display_name":"Schema matching","score":0.6083999872207642},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5230000019073486},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.459199994802475},{"id":"https://openalex.org/keywords/data-extraction","display_name":"Data extraction","score":0.3978999853134155},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.38850000500679016},{"id":"https://openalex.org/keywords/unstructured-data","display_name":"Unstructured data","score":0.3564999997615814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8445000052452087},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.7861999869346619},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.618399977684021},{"id":"https://openalex.org/C2777327318","wikidata":"https://www.wikidata.org/wiki/Q1408390","display_name":"Schema matching","level":3,"score":0.6083999872207642},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5274999737739563},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5230000019073486},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.3978999853134155},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.38850000500679016},{"id":"https://openalex.org/C2781252014","wikidata":"https://www.wikidata.org/wiki/Q1141900","display_name":"Unstructured data","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C68699486","wikidata":"https://www.wikidata.org/wiki/Q265904","display_name":"Document Structure Description","level":3,"score":0.3549000024795532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34299999475479126},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.329800009727478},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C30775581","wikidata":"https://www.wikidata.org/wiki/Q632285","display_name":"Database schema","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C180198813","wikidata":"https://www.wikidata.org/wiki/Q121182","display_name":"Information system","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C153440673","wikidata":"https://www.wikidata.org/wiki/Q7431119","display_name":"Schema migration","level":5,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00008","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00008","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.2607.00008","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00008","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/9","score":0.406947523355484,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Extracting":[0],"structured":[1],"data":[2],"from":[3],"unstructured":[4],"text":[5],"using":[6],"large":[7,17],"language":[8],"models":[9],"(LLMs)":[10],"becomes":[11],"challenging":[12],"when":[13,71],"target":[14],"schemas":[15],"are":[16],"and":[18,32,38,68,79,101],"complex.":[19],"In":[20],"such":[21],"cases,":[22],"including":[23],"the":[24,28,55],"full":[25],"schema":[26,57,66],"in":[27,94,99,105],"prompt":[29],"increases":[30],"cost":[31],"latency,":[33,100],"risks":[34],"lost-in-the-middle":[35],"performance":[36],"degradation,":[37],"can":[39,87],"exceed":[40],"context":[41],"length":[42],"limits.":[43],"We":[44,73],"propose":[45],"SchemaRAG,":[46],"a":[47,96,102],"retrieval-augmented":[48],"generation":[49],"(RAG)":[50],"framework":[51],"that":[52,85],"dynamically":[53],"prunes":[54],"output":[56],"space":[58],"for":[59,111],"schema-conditioned":[60],"information":[61],"extraction":[62],"tasks":[63],"by":[64],"leveraging":[65],"metadata":[67],"few-shot":[69],"examples":[70],"available.":[72],"evaluate":[74],"SchemaRAG":[75,86],"on":[76],"real-world":[77],"healthcare":[78],"e-commerce":[80],"datasets.":[81],"Our":[82],"results":[83],"show":[84],"achieve":[88],"up":[89],"to":[90],"an":[91],"8.8%":[92],"increase":[93],"micro-F1,":[95],"47%":[97],"reduction":[98,104],"48%":[103],"token":[106],"costs,":[107],"demonstrating":[108],"its":[109],"practicality":[110],"large-schema":[112],"extraction.":[113]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}
