Abstract. 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 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 two application scenarios. These scenarios are ontology matching, and knowledge base verification, with a special focus on temporal reasoning. Our results 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. Keywords: Ontologies, Reasoning, Markov Logic, RDF Schema, Log-linear Logics Originally, the idea of the Semantic Web was built on formal ontologies and logical reasoning leading to the development of description logic based ontology languages, in particular the Web Ontology Language OWL. The advantages of logical reasoning on the web has been argued by many researchers in terms of verifying information models and extending query results to implicit information. While in particular, the development of light-weight ontology languages and the use of database techniques for ontology-based data access has given another boost to the use of logical models (e.g. [7] ), it has also become more and more clear that logical reasoning alone is not enough for many Semantic Web applications. In particular, use cases that require a combined processing of structured and unstructured data (i.e. free text) can often not be adequately handled by logical methods alone. On the other hand, abandoning logical reasoning and completely relying on machine learning techniques for handling unstructured data seems like throwing out the child with the bathwater. Therefore, research on the (Semantic) Web has focused on methods that combine structured representations with statistical inference. One possible approach is the extension of machine learning methods to complex structural features [10] , the other is to extend logical reasoning in Semantic Web languages towards statistical inference. In this paper, we present a software infrastructure that implements the second way -an extension of logical reasoning in Semantic Web languages with statistical inference [27] . In particular, we focus on the task of computing the most probable consistent ontology from a set of possibly inconsistent axioms some of which are not certain and have a weight attached that can be used to resolve inconsistencies given a probabilistic interpretation. This corresponds to the maximum a posteriori (MAP) inference in statistical relational models as opposed to marginal inference, which aims at computing the probability of a certain assertion given evidence in terms of a knowledge base. Our approach is based on an encoding of description logics into Markov logic networks that enables reasoning about description logics that support consequence-driven reasoning.
An infrastructure for probabilistic reasoning with ontologies based on a Markov logic engine was recently presented in REF , and applied to different tasks including activity recognition and root cause analysis.
An infrastructure for probabilistic reasoning with web ontologies
Semantic Web
Semantic Web
Philosophy Computer Science