[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83849-en":3,"doc-seo-83849-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83849,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Identifiability of Relational Queries in Multi-View Pretraining","Identifiability of relational queries in multi-view pretraining is studied for data integration through a shared interface. Structural ambiguity is shown to arise from interface design rather than data volume, when multiple globally consistent worlds agree on shared attributes yet differ on query answers. The work formalizes identifiability under interface laws, proves a polynomial-time CheckCert decision method, establishes an irreducible minimax error floor for non-identifiable queries, and presents a Greedy-MinAug algorithm for minimal interface augmentation.","arXiv :2607 .04735v 1 [ cs .DB] 6 Jul 2026  \nIdentifiability of Relational Queries in Multi-View Pretraining  \nRATAN BAHADUR THAPA , University of Stuttgart, Germany DANIEL HERNÁNDEZ , University of Stuttgart, Germany  \nWhen data sources are integrated through a shared interface, a downstream query may or may not be determined by what the interface exposes: two globally consistent worlds can agree on every shared attribute yet disagree on the query answer. This ambiguity is structural—a property of the interface design, not the data volume—and cannot be resolved by collecting more records or training a larger model.  \nWe formalize query identifiability for data integration under interface laws—functional dependencies that hold uniformly across all legal worlds rather than within a single instance—and prove three results. (i) A polynomial-time certificate (CheckCert) decides identifiability via attribute closure, and is exact on instances that actually expose any residual ambiguity (which we formally call closure-separable). (ii) Non-identifiable queries face an irreducible 1/2 minimax error floor for any estimator using only interface evidence, bounding multi-view pretraining systems from below. (iii) A minimum-augmentation algorithm (Greedy-MinAug) finds the smallest set of interface additions to certify a query, reducing the problem to Set Cover (logarithmic approximation) .  \nExperiments on synthetic benchmarks, real integration datasets spanning three domains (scholarly, product, and restaurant), and schemas up to 103 attributes confirm CheckCert is exact, both algorithms run in single-digit milliseconds, and ML classifiers exhibit the predicted error floor and abrupt capability gains that augmentation produces.  \nCCS Concepts: • Theory of computation → Database theory; • Information systems → Data integration; • Computing methodologies → Learning latent representations.  \nAdditional Key Words and Phrases: Query identifiability, Data integration, Interface laws, Conjunctive queries, Multi-view learning  \n1 INTRODUCTION  \nEnterprise analytics increasingly draws from data lakes and federated ecosystems where analysts query across hundreds to thousands of sources with incompatible schemas [29, 34]; at open-data scale, catalogues index tens of millions of datasets from independent producers [7] . In each case an integration layer specifies which attributes are shared across sources and which deterministic correspondences between them hold, but leaves the rest unconstrained. A growing class of representation-learning methods, multi-view and contrastive pretraining, learns directly from these shared attributes, aligning records across sources by the attributes they share [26, 37] . This raises a foundational question prior to any particular model or training procedure: given only what the integration interface exposes, which queries are determined—guaranteed the same answer across every dataset consistent with the interface—and which are irreducibly ambiguous, no matter how much data is gathered or how large the model? We call this property query identifiability, and it is the subject of this paper.  \nWhere does this ambiguity arise? Consider a billing system and a support system integrated through a shared interface. A data engineer needs to answer a query: does this customer have both an overdue invoice and a high-severity support ticket? The billing system identifies customers by email address (email); the support system uses an internal customer code (cid) . Their shared interface aligns records on email—the one attribute they have in common—but says nothing about which cid corresponds to which email. Without that correspondence the interface offers no unique way  \nAuthors’ addresses: Ratan Bahadur Thapa , Analytic Computing, KI, University of Stuttgart, Stuttgart, Germany, ratan. [thapa@ki.uni-stuttgart.de](thapa@ki.uni-stuttgart.de); Daniel Hernández , Analytic Computing, KI, University of Stuttgart, Stuttgart, Germany, da","cbCais2eCmf0rfVw","https://ap.wps.com/l/cbCais2eCmf0rfVw","pdf",823680,1,33,"English","en",105,"# Introduction\n## Query identifiability in data integration\n## Structural ambiguity and interface laws","[{\"question\":\"What does query identifiability mean in data integration interfaces?\",\"answer\":\"A query is identifiable when all legal worlds consistent with the interface return the same answer. If different legal worlds yield different answers, the query is structurally ambiguous.\"},{\"question\":\"Why can ambiguity not be solved by collecting more records or using a larger model?\",\"answer\":\"Because the ambiguity is structural: the shared interface lacks the necessary deterministic correspondences. Without those correspondences, additional data cannot reveal missing mappings.\"},{\"question\":\"How does CheckCert determine whether a query is identifiable?\",\"answer\":\"CheckCert decides identifiability via attribute closure under interface laws. When it fails on instances that expose residual ambiguity, it serves as an exact proof of non-identifiability.\"}]",1784190969,83,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"identifiability-of-relational-queries-in-multi-view-pretraining","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/identifiability-of-relational-queries-in-multi-view-pretraining/83849/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does query identifiability mean in data integration interfaces?","Question",{"text":75,"@type":76},"A query is identifiable when all legal worlds consistent with the interface return the same answer. If different legal worlds yield different answers, the query is structurally ambiguous.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can ambiguity not be solved by collecting more records or using a larger model?",{"text":80,"@type":76},"Because the ambiguity is structural: the shared interface lacks the necessary deterministic correspondences. Without those correspondences, additional data cannot reveal missing mappings.",{"name":82,"@type":73,"acceptedAnswer":83},"How does CheckCert determine whether a query is identifiable?",{"text":84,"@type":76},"CheckCert decides identifiability via attribute closure under interface laws. 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