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Integrating Graded Knowledge and Temporal Change in a Modal Fragment of OWL

  • Hans-Ulrich Krieger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10162)

Abstract

Natural language statements uttered in diagnosis, but more general in daily life are usually graded, i.e., are associated with a degree of uncertainty about the validity of an assessment and is often expressed through specific words in natural language. In this paper, we look into a representation of such graded statements by presenting a simple non-standard modal logic which comes with a set of modal operators, directly associated with the words indicating the uncertainty and interpreted through confidence intervals in the model theory. We complement the model theory by a set of RDFS-/OWL 2 RL-like entailment (if-then) rules, acting on the syntactic representation of modalized statements. After that, we extend the modal statements by transaction time, in order to implement a notion of temporal change. Our interest in such a formalization is related to the use of OWL as the de facto language in today’s ontologies and its weakness to represent and reason about assertional knowledge that is uncertain and that changes over time.

Keywords

Modal Language Modal Extension Valid Time Hybrid Logic Satisfaction Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research described in this paper has been co-funded by the Horizon 2020 Framework Programme of the European Union within the project PAL (Personal Assistant for healthy Lifestyle) under Grant agreement no. 643783 and by the German Federal Ministry of Education and Research (BMBF) through the project HySociaTea (Hybrid Social Teams for Long-Term Collaboration in Cyber-Physical Environments, grant no. 01IW14001). My colleagues Miroslav Janíček, Bernd Kiefer, and Stefan Schulz have lend me their ears and I have profited from discussions with them. I would also like to thank the ICAART reviewers for their detailed and useful suggestions—thank you all guys!

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.German Research Center for Artificial Intelligence (DFKI)SaarbrückenGermany

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