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    Item type:Publication,
    Reasoning in Many Dimensions : Uncertainty and Products of Modal Logics
    Probabilistic Description Logics (ProbDLs) are an extension of Description Logics that are designed to capture uncertainty. We study problems related to these logics. First, we investigate the monodic fragment of Probabilistic first-order logic, show that it has many nice properties, and are able to explain the complexity results obtained for ProbDLs. Second, in order to identify well-behaved, in best-case tractable ProbDLs, we study the complexity landscape for different fragments of ProbEL; amongst others, we are able to identify a tractable fragment. We then study the reasoning problem of ontological query answering, but apply it to probabilistic data. Therefore, we define the framework of ontology-based access to probabilistic data and study the computational complexity therein. In the final part of the thesis, we study the complexity of the satisfiability problem in the two-dimensional modal logic KxK. We are able to close a gap that has been open for more than ten years.
    doctoral thesis
      502  100
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    Item type:Publication,
    Actively Learning ELI Queries under DL-Lite Ontologies
    We show that ELI queries (ELIQs) are learnable in polynomial time in the presence of a DL-Lite ontology O, in Angluin’s framework of active learning. When initially provided with a conjunctive query (CQ) that implies the target ELIQ under O (in the sense of query containment), it suffices for the learner to only pose membership queries to the oracle, but no equivalence queries. The initial CQ can be obtained by a single equivalence query and is available ‘for free’ in case that O does not pose any disjointness constraints on concepts. Our main technical result is that every ELI concept has only polynomially many most specific subsumers w.r.t. a DL-Lite ontology, generalizing a recent result about homomorphism frontiers by ten Cate and Dalmau.
    conference paper
      18  25