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Auflistung nach Autor:in "Dylla, Maximilian"

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  • Konferenzbeitrag
    An overview on querying and learning in temporal probabilistic databases
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Dylla, Maximilian
    Probabilistic databases store, query and manage large amounts of uncertain information in an efficient way. This paper summarizes my thesis which advances the state-of-the-art in probabilistic databases in three different ways: First, we present a closed and complete data model for temporal probabilistic databases. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty. Second, we devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Moreover, we derive probability bounds on these formulas which enable pruning low-probability answers. Third, we introduce the problem of learning tuple probabilities, which allows updating and cleaning of probabilistic databases, and study its complexity and characterize its solutions.
  • Konferenzbeitrag
    Resolving temporal conflicts in inconsistent RDF knowledge bases
    (Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Dylla, Maximilian; Sozio, Mauro; Theobald, Martin
    Recent trends in information extraction have allowed us to not only extract large semantic knowledge bases from structured or loosely structured Web sources, but to also extract additional annotations along with the RDF facts these knowledge bases contain. Among the most important types of annotations are spatial and temporal annotations. In particular the latter temporal annotations help us to reflect that a majority of facts is not static but highly ephemeral in the real world, i.e., facts are valid for only a limited amount of time, or multiple facts stand in temporal dependencies with each other. In this paper, we present a declarative reasoning framework to express and process temporal consistency constraints and queries via first-order logical predicates. We define a subclass of first-order constraints with temporal predicates for which the knowledge base is guaranteed to be satisfiable. Moreover, we devise efficient grounding and approximation algorithms for this class of first order constraints, which can be solved within our framework. Specifically, we reduce the problem of finding a consistent subset of time-annotated facts to a scheduling problem and give an approximation algorithm for it. Experiments over a large temporal knowledge base (T-YAGO) demonstrate the scalability and excellent approximation performance of our framework.