License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICDT.2021.9
URN: urn:nbn:de:0030-drops-137172
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/13717/
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ten Cate, Balder ; Dalmau, Victor

Conjunctive Queries: Unique Characterizations and Exact Learnability

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LIPIcs-ICDT-2021-9.pdf (0.9 MB)

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.

BibTeX - Entry

@InProceedings{tencate_et_al:LIPIcs.ICDT.2021.9,
  author =	{ten Cate, Balder and Dalmau, Victor},
  title =	{{Conjunctive Queries: Unique Characterizations and Exact Learnability}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{9:1--9:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13717},
  URN =		{urn:nbn:de:0030-drops-137172},
  doi =		{10.4230/LIPIcs.ICDT.2021.9},
  annote =	{Keywords: Conjunctive Queries, Homomorphisms, Frontiers, Unique Characterizations, Exact Learnability, Schema Mappings, Description Logic}
}

Keywords: Conjunctive Queries, Homomorphisms, Frontiers, Unique Characterizations, Exact Learnability, Schema Mappings, Description Logic
Collection: 24th International Conference on Database Theory (ICDT 2021)
Issue Date: 2021
Date of publication: 11.03.2021

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