COBRA, a Demo

COBRA is a tool for performing context-based reasoning in OWL ontologies, with an emphasis on its applications. COBRA is built on top of standard OWL reasoning tools, and uses the OWL-API, which makes it easy to use, maintain, and keep up-to-date with reasoning technologies. To understand the behaviour of COBRA, we must first explain the idea of context-based reasoning. We consider as input for the system an annotated OWL ontology. Intuitively, this annotated ontology is a compact representation of a class of ontologies, which we call contexts. More precisely, every axiom in an OWL ontology is annonated, using the owl:annotation tag, with a list of labels that express the contexts that this axiom belongs to. For example, if we have two contexts C1 and C2 containing axioms α1 and α2; and α2 and α3, respectively, then the annotated input ontology O will contain three axioms α1, α2, and α3 annotated with {c1}, {c1, c2}, and {c2}, respectively. One of the main goals of context-based reasoning is to identify the class of contexts that entail a given logical consequence. An obvious method for solving this reasoning problem would be to first extract all the different contexts from the annotated ontology, and perform standard reasoning in all of them. Given the speed of state-of-the-art OWL reasoners, this approach, in the following called the näıve approach produces a very efficient reasoning procedure for realistic OWL ontologies, assuming that the number of contexts is small. However, as the number of contexts grows, so does the total execution time of the näıve method, by a linear factor. To handle cases where the number of contexts is high (e.g., above a thousand) more effective methods based on axiom-pinpointing, for detecting large clusters of contexts entailing the consequence, through only one call to the reasoner, and a variant of Reiter’s Hitting Set Tree (HST) algorithm [3] for ensuring that all possible contexts have been detected through a systematic search. Specifically, we use the HST approach for computing the so-called boundary of a consequence described in [1]. Notice that in this case, the HST approach can be further optimized, since the background lattice required in [1] corresponds to the class of all sets of labels, with union and intersection as operators, which are easier to compute. COBRA implements the näıve and the HST methods via a sequence of blackbox calls to standard OWL reasoners. For the HST method, it must call a rea-