1. Conjunctive Queries: Unique Characterizations and Exact Learnability
- Author
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ten Cate, B., Dalmau, V., Yi, K., Wei, Z., ILLC (FNWI), and Logic and Computation (ILLC, FNWI/FGw)
- Subjects
FOS: Computer and information sciences ,Computer Science - Logic in Computer Science ,I.2.4 ,Theory of computation → Machine learning theory ,Computer Science - Artificial Intelligence ,I.2.6 ,H.2.3 ,68Q32 ,Schema Mappings ,Conjunctive Queries ,Unique Characterizations ,Databases (cs.DB) ,Description Logic ,Homomorphisms ,Theory of computation → Logic ,Logic in Computer Science (cs.LO) ,Frontiers ,Artificial Intelligence (cs.AI) ,Computer Science - Databases ,Exact Learnability ,Information systems → Query languages ,Information Systems - Abstract
We answer the question of which conjunctive queries are uniquely characterized by polynomially many positive and negative examples, and how to construct such examples efficiently. As a consequence, we obtain a new efficient exact learning algorithm for a class of conjunctive queries. At the core of our contributions lie two new polynomial-time algorithms for constructing frontiers in the homomorphism lattice of finite structures. We also discuss implications for the unique characterizability and learnability of schema mappings and of description logic concepts., LIPIcs, Vol. 186, 24th International Conference on Database Theory (ICDT 2021), pages 9:1-9:24
- Published
- 2022
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