KR2022Proceedings of the 19th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning

Haifa, Israel. July 31–August 5, 2022.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-01-0

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Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization

On the Relationship between Shy and Warded Datalog+/-

  1. Teodoro Baldazzi(Università Roma Tre, Italy)
  2. Luigi Bellomarini(Bank of Italy)
  3. Marco Favorito(Bank of Italy, Università Sapienza, Italy)
  4. Emanuel Sallinger(TU Wien, Austria, University of Oxford, UK)


  1. Knowledge representation languages
  2. Ontology formalisms and models
  3. Reasoning and learning over knowledge graphs


Datalog^E is the extension of Datalog with existential quantification. While its high expressive power, underpinned by a simple syntax and the support for full recursion, renders it particularly suitable for modern applications on Knowledge Graphs, query answering (QA) over such language is known to be undecidable in general. For this reason, different fragments have emerged, introducing syntactic limitations to Datalog^E that strike a balance between its expressive power and the computational complexity of QA, to achieve decidability. In this short paper, we focus on two promising tractable candidates, namely Shy and Warded Datalog+/-. Reacting to an explicit interest from the community, we shed light on the relationship between these fragments. Moreover, we carry out an experimental analysis of the systems implementing Shy and Warded, respectively DLV^E and Vadalog.