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Description Logics with Abstraction and Refinement (2306.03717v3)

Published 6 Jun 2023 in cs.AI and cs.LO

Abstract: Ontologies often require knowledge representation on multiple levels of abstraction, but description logics (DLs) are not well-equipped for supporting this. We propose an extension of DLs in which abstraction levels are first-class citizens and which provides explicit operators for the abstraction and refinement of concepts and roles across multiple abstraction levels, based on conjunctive queries. We prove that reasoning in the resulting family of DLs is decidable while several seemingly harmless variations turn out to be undecidable. We also pinpoint the precise complexity of our logics and several relevant fragments.

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References (22)
  1. 1996. Part-whole relations in object-centered systems: An overview. Data & Knowledge Engineering 20(3):347–383.
  2. 2017. An Introduction to Description Logic. Cambridge University Press.
  3. 2003. A theory of granular partitions. In Foundations of Geographic Information Science. Taylor & Francis. 117–149.
  4. 2010. Granular computing applied to ontologies. Int. J. Approx. Reason. 51(4):391–409.
  5. 1981. Alternation. Journal of the ACM (JACM) 28(1):114–133.
  6. 2022. Monotone abstractions in ontology-based data management. In Proc. of AAAI, 5556–5563. AAAI Press.
  7. 2018. Abstraction and abstraction refinement. In Handbook of Model Checking. Springer. 385–419.
  8. 2002. Semantic granularity in ontology-driven geographic information systems. Ann. Math. Artif. Intell. 36(1-2):121–151.
  9. 2003. Understanding the semantic web through descriptions and situations. In Proc. of OTM, volume 2888 of LNCS, 689–706. Springer.
  10. 2017. Ontology materialization by abstraction refinement in horn SHOIF. In Proc. of AAAI, 1114–1120. AAAI Press.
  11. 2021. Connecting granular and topological relations through description logics (short paper). In Proc. of the GeoLD Workshop, volume 2977 of CEUR Workshop Proceedings, 97–104. CEUR-WS.org.
  12. Hobbs, J. R. 1985. Granularity. In Proc. of IJCAI, 432–435. Morgan Kaufmann.
  13. Keet, C. M. 2008. A Formal Theory of Granularity. Ph.D. Dissertation, KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano.
  14. 2016. Description logics of context. J. Log. Comput. 26(3):817–854.
  15. 2008. Interval rough mereology and description logic: An approach to formal treatment of imprecision in the semantic web ontologies. Web Intell. Agent Syst. 6(2):157–174.
  16. 2018. A granular computing method for OWL ontologies. Fundam. Informaticae 159(1-2):147–174.
  17. Lutz, C. 2008. The complexity of conjunctive query answering in expressive description logics. In Proc. of IJCAR, volume 5195 of LNCS, 179–193. Springer.
  18. Manière, Q. 2022. Counting queries in ontology-based data access. Ph.D. Dissertation, Université de Bordeaux.
  19. 2008. How granularity issues concern biomedical ontology integration. In Proc. of MIE, volume 136 of Studies in Health Technology and Informatics, 863–868. IOS Press.
  20. 2013. Unary negation. Log. Methods Comput. Sci. 9(3).
  21. 2001. SNOMED clinical terms: overview of the development process and project status. In Proc. of AMIA.
  22. Vogt, L. 2019. Levels and building blocks - toward a domain granularity framework for the life sciences. J. Biomed. Semant. 10(1):4:1–4:29.
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Authors (2)
  1. Carsten Lutz (50 papers)
  2. Lukas Schulze (2 papers)

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