Conjunctive Queries: Unique Characterizations and Exact Learnability
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Abstract (arXiv)
We answer the question 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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1.
6 papers.
- 2023SAT-Based PAC Learning of Description Logic Conceptscited by 5
- 2021Actively Learning Concepts and Conjunctive Queries under ELr-Ontologiescited by 3
- 2022Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologiescited by 3
- 2022On the non-efficient PAC learnability of conjunctive queriescited by 2
- 2024On the Power and Limitations of Examples for Description Logic Conceptscited by 2
- 2023Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontologycited by 0
- 2024On the Power and Limitations of Examples for Description Logic Concepts23 Dec 2024
- 2023Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology13 Jun 2023
- 2023SAT-Based PAC Learning of Description Logic Concepts15 May 2023
- 2022On the non-efficient PAC learnability of conjunctive queries22 Aug 2022
- 2022Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies29 Apr 2022
- 2021Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies18 May 2021