Communications of the ACM
Information Processing Letters
Negative Results for Equivalence Queries
Machine Learning
The Strength of Weak Learnability
Machine Learning
Discrete Applied Mathematics
Computational learning theory: an introduction
Computational learning theory: an introduction
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Pac-learning non-recursive Prolog clauses
Artificial Intelligence
How many queries are needed to learn?
Journal of the ACM (JACM)
Conjunctive query containment revisited
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
Conjunctive-query containment and constraint satisfaction
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Data-driven understanding and refinement of schema mappings
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Machine Learning
On the Hardness of Learning Acyclic Conjunctive Queries
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Learning to map between structured representations of data
Learning to map between structured representations of data
Schema mappings, data exchange, and metadata management
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clio grows up: from research prototype to industrial tool
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
HePToX: marrying XML and heterogeneity in your P2P databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Data exchange: semantics and query answering
Theoretical Computer Science - Database theory
Database dependency discovery: a machine learning approach
AI Communications
The complexity of properly learning simple concept classes
Journal of Computer and System Sciences
Implementing mapping composition
The VLDB Journal — The International Journal on Very Large Data Bases
Hardness of approximate two-level logic minimization and PAC learning with membership queries
Journal of Computer and System Sciences
Muse: Mapping Understanding and deSign by Example
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Discovering Conditional Functional Dependencies
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
On the Expressive Power of the Relational Algebra on Finite Sets of Relation Pairs
IEEE Transactions on Knowledge and Data Engineering
Logical foundations of relational data exchange
ACM SIGMOD Record
Pac-learning recursive logic programs: efficient algorithms
Journal of Artificial Intelligence Research
Pac-learning recursive logic programs: negative results
Journal of Artificial Intelligence Research
Learning disjunction of conjunctions
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Schema mapping discovery from data instances
Journal of the ACM (JACM)
Synthesizing view definitions from data
Proceedings of the 13th International Conference on Database Theory
Characterizing schema mappings via data examples
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Database constraints and homomorphism dualities
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Designing and refining schema mappings via data examples
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Schema mappings and data examples
Proceedings of the 16th International Conference on Extending Database Technology
Learning and verifying quantified boolean queries by example
Proceedings of the 32nd symposium on Principles of database systems
Learning queries for relational, semi-structured, and graph databases
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
ACM Transactions on Database Systems (TODS) - Invited papers issue
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A schema mapping is a high-level specification of the relationship between a source schema and a target schema. Recently, a line of research has emerged that aims at deriving schema mappings automatically or semi-automatically with the help of data examples, i.e., pairs consisting of a source instance and a target instance that depict, in some precise sense, the intended behavior of the schema mapping. Several different uses of data examples for deriving, refining, or illustrating a schema mapping have already been proposed and studied. In this paper, we use the lens of computational learning theory to systematically investigate the problem of obtaining algorithmically a schema mapping from data examples. Our aim is to leverage the rich body of work on learning theory in order to develop a framework for exploring the power and the limitations of the various algorithmic methods for obtaining schema mappings from data examples. We focus on GAV schema mappings, that is, schema mappings specified by GAV (Global-As-View) constraints. GAV constraints are the most basic and the most widely supported language for specifying schema mappings. We present an efficient algorithm for learning GAV schema mappings using Angluin's model of exact learning with membership and equivalence queries. This is optimal, since we show that neither membership queries nor equivalence queries suffice, unless the source schema consists of unary relations only. We also obtain results concerning the learnability of schema mappings in the context of Valiant's well known PAC (Probably-Approximately-Correct) learning model. Finally, as a byproduct of our work, we show that there is no efficient algorithm for approximating the shortest GAV schema mapping fitting a given set of examples, unless the source schema consists of unary relations only.