Abstract
This paper proposes approximate algorithms for computing inconsistency degree and answering instance checking queries in the framework of uncertain lightweight ontologies. We focus on an extension of lightweight ontologies, encoded here in DL-Lite languages, to the product-based possibility theory framework. We provide an encoding of the problem of computing inconsistency degree in product-based possibility DL-Lite as a weighted set cover problem and we use a greedy algorithm to compute an approximate value of the inconsistency degree. We also propose an approximate algorithm for answering instance checking queries in product-based possibilistic DL-Lite. Experimental studies show the good results obtained by both algorithms.
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Acknowledgments
This work has been 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 and from ASPIQ project reference ANR-12-BS02-0003 of French National Research Agency.
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Benferhat, S., Boutouhami, K., Khellaf, F., Nouioua, F. (2016). Algorithms for Quantitative-Based Possibilistic Lightweight Ontologies. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_31
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DOI: https://doi.org/10.1007/978-3-319-42007-3_31
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