DL reasoners are complex pieces of software that work on even more complex input which makes manual verification difficult. A single ontology can have hundreds or thousands of classes and thus its classification involve an unsurveyable number of subsumption tests. We propose a new method for debugging classification across multiple reasoners which employs justifications generated from the set of entailments that reasoners disagree upon to determine the cause of the disagreement.
This paper evaluates the performance of the OWL 2 reasoners Pellet and HermiT in an eHealth context where most of the ABox is considered static and discrete transient events describing the environment are incrementally added and processed. The considered use case is the assignment of tasks and calls to nurses. To provide personalized and optimized care, the selection process utilizes reasoning to make intelligent assignment decisions based on the available information. This has been implemented using multiple SPARQL-queries to enable easy adaptation of the assignment algorithm. Since limited time is available to perform the assignments, the decision should be made in at most five seconds. An analysis of the performance and scalability of the reasoners is presented. To deal with the limited time frame, several optimizations are suggested, which exploit that most of the ABox is considered static.
We present a survey of the current OWL reasoner landscape. Through literature and web search we have identified 35 OWL reasoners that are, at least to some degree, actively maintained. We conducted a survey directly addressing the respective developers, and collected 33 responses. We present an analysis of the survey, characterising all reasoners across a wide range of categories such as supported expressiveness and reasoning services. We will also provide some insight about ongoing research efforts and a rough categorisation of reasoner calculi.
The advantage of using semantic reasoners based on Description Logics (DL) for the development of intelligent systems is doubtless: They make easy the optimal management of knowledge (expressed as ontologies). Reasoning is a complex and computationally expensive task traditionally performed on powerful server and desktop computers. However, we should not discard reasoners from being used on mobile devices such as smartphones or tablets, as the increasing number of applications run on wireless environments demands that many intelligent tasks should be performed on mobile devices rather than on desktop or server computers. Although less powerful that their fixed counterparts, mobile devices are becoming more and more capable of running complex tasks, such as DL reasoning. In this paper we introduce the framework of a competition for reasoners on mobile devices, based on the OWL Reasoner Evaluation, with the main goal of promoting the development of reasoners adapted to mobile environments. We detail the format of such a competition including the kind of tasks that participating reasoners should execute, interesting results of such executions, and the kind of ontologies and devices that could be used to run such tests.
Bayesian ontology languages are a family of probabilistic ontology languages that allow to encode probabilistic information over the axioms of an ontology with the help of a Bayesian network. The Bayesian ontology language BEL is an extension of the lightweight Description Logic (DL) EL within the above-mentioned framework. We present the system BORN that implements the probabilistic subsumption problem for BEL.
