KR2020Proceedings of the 17th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 12-18, 2020.

Edited by

ISSN: 2334-1033
ISBN: 978-0-9992411-7-2

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Published by

Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization

Balancing Expressiveness and Inexpressiveness in View Design

  1. Michael Benedikt(University of Oxford)
  2. Pierre Bourhis(CRIStAL, CNRS, University of Lille, INRIA)
  3. Louis Jachiet(LTCI IP Paris)
  4. Efthymia Tsamoura(Samsung AI Research)


  1. Knowledge representation languages-General
  2. Ontology-based data access, integration, and exchange-General


We study the design of data publishing mechanisms

that allow a collection of autonomous distributed datasources to collaborate to support queries.

A common mechanism for data publishing is via views: functions that expose

derived data to users, usually specified as declarative queries. Our autonomy assumption is

that the views must be on individual sources, but with the intention of supporting integrated


In deciding what data to expose to users, two considerations must be balanced. The views must be sufficiently expressive

to support queries that users want to ask -- the utility of the publishing mechanism.

But there may also be some expressiveness restriction. Here we consider two restrictions,

a minimal information requirement, saying that the views should reveal as little as possible while supporting the utility

query, and a non-disclosure requirement, formalizing the need to prevent external users from computing information that data owners do not want revealed.

We investigate the problem of designing views that satisfy both an expressiveness and an inexpressiveness requirement,

for views in a restricted declarative language

(conjunctive queries), and for arbitrary views.