Joerg Schoenfisch

IT Risk Management with Markov Logic Networks

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 they can be translated into an MLN. The MLN infrastructure model allows us to use marginal inference to predict the availability of IT infrastructure components and services. We demonstrate that our solution is well suited for supporting IT Risk management by analyzing the impact of threats and comparing risk mitigation efforts.

An infrastructure for probabilistic reasoning with web ontologies

We present an infrastructure for probabilistic reasoning with ontologies 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 of formulas. The resulting system is an infrastructure for log linear logics that can be used for probabilistic reasoning with both extended OWL-EL and RDF schema. We describe our system and illustrate its benefits by presenting experimental results for two types of applications. These are ontology matching and knowledge base verification, with a special focus on temporal reasoning. Moreover, we illustrate two further use cases which are Activity Recognition and Root Cause Analysis. Our infrastructure has been applied to these use cases in the context of a cooperation with industry partners. The experiments indicate that our system, which is based on a well-founded probabilistic semantics, is capable of solving relevant problems as good as or better than state of the art systems that have specifically been designed for the respective problem. The heterogeneity of the presented uses cases illustrates the wide applicability of our infrastructure.

Towards large-scale probabilistic OBDA

Ontology-based Data Access has intensively been studied as a very relevant problem in connection with semantic web data. Often it is assumed, that the accessed data behaves like a classical database, i.e. it is known which facts hold for certain. Many Web applications, especially those involving information extraction from text, have to deal with uncertainty about the truth of information. In this paper, we introduce an implementation and a benchmark of such a system on top of relational databases. Furthermore, we propose a novel benchmark for systems handling large probabilistic ontologies. We describe the benchmark design and show its characteristics based on the evaluation of our implementation.

T e C o R e : temporal conflict resolution in knowledge graphs

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs (kgs), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kgs focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kgs are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate TeCoRe, a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs (utkgs). At the heart of TeCoRe are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkgs. TeCoRe provides an interface allowing to select utkgs and editing constraints; shows the maximal consistent subset of the utkg, and displays statistics (e.g., number of noisy facts removed) about the debugging process.

Root Cause Analysis in IT Infrastructures using Ontologies and Abduction in Markov Logic Networks

Information systems play a crucial role in most of today’s business operations. High availability and reliability of services and hardware, and, in the case of outages, short response times are essential. Thus, a high amount of tool support and automation in risk management is desirable to decrease downtime. We propose a new approach for calculating the root cause for an observed failure in an IT infrastructure. Our approach is based on abduction in Markov Logic Networks. Abduction aims to find an explanation for a given observation in the light of some background knowledge. In failure diagnosis, the explanation corresponds to the root cause, the observation to the failure of a component, and the background knowledge to the dependency graph extended by potential risks. We apply a method to extend a Markov Logic Network in order to conduct abductive reasoning, which is not naturally supported in this formalism. Our approach exhibits a high amount of reusability and facilitates modeling by using ontologies as background knowledge. This enables users without spe- cific knowledge of a concrete infrastructure to gain viable insights in the case of an incident. We implemented the method in a tool and illustrate its suitabil- ity for root cause analysis by applying it to a sample scenario and testing its scalability on randomly generated infrastructures.