Abstract
In different situations, information coming from different sources are often affected with uncertainty and imprecision. Representing such information generally gives rise to a prioritized (i.e. stratified) knowledge base. To reason with such prioritized knowledge in a principled way, we propose an extension of \(\mathcal {EL}\) description logics within possibility theory, which provides a very natural framework to deal with ordinal, qualitative uncertainty, preferences and priorities. We first introduce the syntax and semantics of possibilistic \(\mathcal {EL}\), and then provide the main related reasoning tasks. We show in particular that these tasks remain tractable in possibilistic \(\mathcal {EL}\).
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Notes
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In fact, it is a mapping from \(\varOmega \) to a totally ordered scale O. This scale may often be a finite set of integers or the unit interval \(\left[ 0,1\right] \) and encodes our knowledge on the real world. In general, one considers the interval \(\left[ 0,1\right] \).
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Acknowledgments
This work is supported by the European project H2020 Marie Sklodowska-Curie Actions (MSCA) research and Innovation Staff Exchange (RISE): AniAge (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage.
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Mohamed, R., Loukil, Z., Bouraoui, Z. (2018). Qualitative-Based Possibilistic \(\mathcal {EL}\) Ontology. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_41
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DOI: https://doi.org/10.1007/978-3-030-03098-8_41
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