Joerg Schoenfisch

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We present a solution for modeling the dependencies of an IT infrastructure and determine the availability of components and services therein using Markov logic networks (MLN). MLNs offer a single representation of probability and first-order logic and are well suited to model dependencies and threats. We identify different kinds of dependency and show how(More)
We present an infrastructure for probabilistic reasoning with ontologies that is based on our Markov logic engine ROCKIT. Markov logic is a template language that combines first-order logic with log-linear graphical models. We show how to translate OWL-EL as well as RDF schema to Markov logic and how to use ROCKIT for applying MAP inference on the given set(More)
Handling uncertain knowledge – like information extracted from un-structured text, with some probability of being correct – is crucial for modeling many real world domains. Ontologies and ontology-based data access (OBDA) have proven to be versatile methods to capture this knowledge. Multiple systems for OBDA have been developed and there is theoretical(More)
Pneumatic nail guns have a safety device (workpiece contact, nose, yoke, tip) at the end of the gun muzzle that must be depressed before the fastener can be discharged. Two types of trigger systems define how the nail gun fires in response to a trigger press: 1) The sequential actuation trigger requires that each nail can only be discharged when the safety(More)
In this paper we propose an approach for calculating the most probable root cause for an observed failure in an IT infrastructure. Our approach is based on Markov Logic Networks. While Markov Logic supports a special type of deductive inference, known as maximum a posteriori inference, the computation of the most probable cause requires abductive reasoning.(More)
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