{"id":"https://openalex.org/W7162990467","doi":"https://doi.org/10.48550/arxiv.2605.30729","title":"SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching","display_name":"SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7162990467","doi":"https://doi.org/10.48550/arxiv.2605.30729"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.30729","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137598533","display_name":"Inwon Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Inwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085594669","display_name":"Kavitha Srinivas","orcid":"https://orcid.org/0000-0003-4610-967X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srinivas, Kavitha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079757419","display_name":"Nandana Mihindukulasooriya","orcid":"https://orcid.org/0000-0003-1707-4842"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mihindukulasooriya, Nandana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134502264","display_name":"Sola Shirai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shirai, Sola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089095944","display_name":"Parikshit Ram","orcid":"https://orcid.org/0000-0002-9456-029X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ram, Parikshit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035277014","display_name":"Horst Samulowitz","orcid":"https://orcid.org/0000-0002-6780-3217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samulowitz, Horst","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137546467","display_name":"Oshani Seneviratne","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seneviratne, Oshani","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":0.7544999718666077,"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":0.7544999718666077,"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/T10028","display_name":"Topic Modeling","score":0.1014999970793724,"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":0.05469999834895134,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/serialization","display_name":"Serialization","score":0.6277999877929688},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.4650999903678894},{"id":"https://openalex.org/keywords/joins","display_name":"Joins","score":0.36880001425743103},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3671000003814697},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.36480000615119934},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.3458999991416931},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.32919999957084656},{"id":"https://openalex.org/keywords/wordnet","display_name":"WordNet","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8374999761581421},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.6277999877929688},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3774000108242035},{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C48105269","wikidata":"https://www.wikidata.org/wiki/Q1141160","display_name":"Header","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C2777327318","wikidata":"https://www.wikidata.org/wiki/Q1408390","display_name":"Schema matching","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.29760000109672546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Schema":[0],"matching":[1],"is":[2],"a":[3,72,97,142],"fundamental":[4],"step":[5],"in":[6,193],"integrating":[7],"heterogeneous":[8,98],"data":[9,28],"sources.":[10],"While":[11],"Pre-trained":[12],"Language":[13],"Models":[14],"(PLMs)":[15],"have":[16],"revolutionized":[17],"this":[18,67],"task":[19],"by":[20,107],"capturing":[21],"linguistic":[22],"semantics,":[23],"they":[24],"typically":[25],"process":[26],"tabular":[27],"as":[29,96,179],"serialized":[30],"text":[31],"sequences":[32],"of":[33,79,87,130,189],"standalone":[34,63],"column":[35,59],"descriptions.":[36],"This":[37],"serialization":[38],"discards":[39],"critical":[40],"structural":[41,84,144,191],"information":[42],"--":[43,52],"specifically,":[44],"the":[45,49,76,83,94,110,117,135,149,187],"row-level":[46],"co-occurrences,":[47],"i.e.":[48],"relational":[50],"context":[51,115],"forcing":[53],"models":[54],"to":[55,112],"rely":[56],"solely":[57],"on":[58,148,164],"header":[60],"semantics":[61],"or":[62],"distributions.":[64],"To":[65],"bridge":[66],"gap,":[68],"we":[69],"propose":[70],"SemStruct,":[71],"framework":[73],"that":[74,123,155,174],"joins":[75],"semantic":[77,184],"power":[78],"frozen":[80,138],"PLMs":[81],"with":[82],"inductive":[85],"bias":[86],"Graph":[88],"Neural":[89],"Networks":[90],"(GNNs).":[91],"We":[92],"model":[93,137],"table":[95],"graph":[99],"where":[100],"columns":[101],"and":[102,128,139,151],"values":[103],"are":[104],"nodes":[105],"connected":[106],"rows,":[108],"allowing":[109],"GNN":[111],"propagate":[113],"disambiguating":[114],"across":[116],"structure.":[118],"Unlike":[119],"other":[120],"state-of-the-art":[121,158],"methods":[122],"require":[124],"proprietary":[125],"LLM":[126],"access":[127],"fine-tuning":[129],"language":[131,136],"models,":[132],"SemStruct":[133,156],"keeps":[134],"trains":[140],"only":[141],"lightweight":[143],"encoder.":[145],"Extensive":[146],"experiments":[147],"Valentine":[150],"SOTAB-SM":[152],"benchmarks":[153],"demonstrate":[154],"achieves":[157],"performance,":[159],"outperforming":[160],"fully":[161],"fine-tuned":[162],"baselines":[163],"complex,":[165],"semantically":[166],"joinable":[167],"datasets.":[168],"Furthermore,":[169],"our":[170],"ablation":[171],"studies":[172],"reveal":[173],"row":[175],"representations":[176],"serve":[177],"primarily":[178],"topological":[180],"conduits":[181],"rather":[182],"than":[183],"entities,":[185],"validating":[186],"necessity":[188],"explicit":[190],"modeling":[192],"schema":[194],"matching.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
