Optimising ontology classification

2006

Contextual ontologies are ontologies that characterize a concept by a set of properties that vary according to context. Contextual ontologies are now crucial for users who intend to exchange information in a domain. Existing ontology languages are not capable of defining such type of ontologies. The objective of this paper is to formally define a contextual ontology language to support the development of contextual ontologies. In this paper, we use description logics as an ontology language and then we extend it by introducing a new contextual constructor.

2000

This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA’s Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.

Ontology classification-the computation of subsumption hierarchies for classes and properties-is one of the most important tasks for OWL reasoners. Based on the algorithm by Shearer and Horrocks [1], we present a new classification procedure that addresses several open issues of the original algorithm, and that uses several novel optimisations in order to achieve superior performance. We also consider the classification of (object and data) properties. We show that algorithms commonly used to implement that task are incomplete even for relatively weak ontology languages. Furthermore, we show how to reduce the property classification problem into a standard (class) classification problem, which allows reasoners to classify properties using our optimised procedure. We have implemented our algorithms in the OWL HermiT reasoner, and we present the results of a performance evaluation.

comlab.ox.ac.uk

The problem of computing all subclass relationships between classes and properties is one of the most important reasoning tasks for OWL ontologies. In this paper, we propose a new class classification algorithm mainly motivated by the previous work in [1]. We close several gaps that were left open in the original paper and provide average optimal strategies. We then turn our attention to (object and data) property classification and show that the commonly used algorithms for property classification are incomplete for OWL 2 ontologies. We propose an encoding of the property classification problems into class classification problems, which allows us to take advantage of all the optimisations developed for class classification. We implemented all of the proposed techniques in the OWL reasoner 1.2.4 and compare this version to Her-miT 1.2.2 to evaluate the performance improvements.

2004

In this paper we demonstrate a potential extension of formal verification methodology in order to deal with time-domain properties of analog and mixed-signal circuits whose dynamic behavior is described by differential algebraic equations. To model and analyze such circuits under all possible input signals and all values of parameters, we build upon two techniques developed in the context of hybrid (discrete-continuous) control systems. First, we extend our algorithm for approximating sets of reachable sets for dense-time continuous systems to deal with differential algebraic equations (DAEs) and apply it to a biquad low-pass filter. To analyze more complex circuits, we resort to bounded horizon verification. We use optimal control techniques to check whether a Δ-Σ modulator, modeled as a discrete-time hybrid automaton, admits an input sequence of bounded length that drives it to saturation.

2010

We study Facility Location games, where a number of facilities are placed in a metric space based on locations reported by strategic agents. A mechanism maps the agents’ locations to a set of facilities. The agents seek to minimize their connection cost, namely the distance of their true location to the nearest facility, and may misreport their location. We are interested in mechanisms that are strategyproof, i.e., ensure that no agent can benefit from misreporting her location, do not resort to monetary transfers, and approximate the optimal social cost. We focus on the closely related problems of k-Facility Location and Facility Location with a uniform facility opening cost, and mostly study winner-imposing mechanisms, which allocate facilities to the agents and require that each agent allocated a facility should connect to it. We show that the winner-imposing version of the Proportional Mechanism (Lu et al., EC ’10) is stategyproof and 4k-approximate for the k-Facility Location game. For the Facility Location game, we show that the winner-imposing version of the randomized algorithm of (Meyerson, FOCS ’01), which has an approximation ratio of 8, is strategyproof. Furthermore, we present a deterministic non-imposing group strategyproof O(logn)-approximate mechanism for the Facility Location game on the line.

2012

Ontology classification–the computation of the subsumption hierarchies for classes and properties–is a core reasoning service provided by all OWL reasoners known to us. A popular algorithm for computing the class hierarchy is the so-called Enhanced Traversal (ET) algorithm. In this paper, we present a new classification algorithm that attempts to address certain shortcomings of ET and improve its performance. Apart from classification of classes, we also consider object and data property classification.

Abstract. HermiT is the only reasoner we know of that fully supports the OWL 2 standard, and that correctly reasons about properties as well as classes. It is based on a novel “hypertableau” calculus that addresses performance problems due to nondeterminism and model size—the primary sources of complexity in state-of-the-art OWL reasoners. HermiT also incorporates a number of novel optimizations, including an optimized ontology classification procedure.

2009

Abstract. One of the core services provided by OWL reasoners is classification: the discovery of all subclass relationships between class names occurring in an ontology. Discovering these relations can be computationally expensive, particularly if individual subsumption tests are costly or if the number of class names is large. We present a classification algorithm which exploits partial information about subclass relationships to reduce both the number of individual tests and the cost of working with large ontologies.

ESWC, 2013

Ontology classification is the reasoning service that computes all subsumption relationships inferred in an ontology between concept, role, and attribute names in the ontology signature. OWL 2 QL is a tractable profile of OWL 2 for which ontology classification is polynomial in the size of the ontology TBox. However, to date, no efficient methods and implementations specifically tailored to OWL 2 QL ontologies have been developed. In this paper, we provide a new algorithm for ontology classification in OWL 2 QL, which is based on the idea of encoding the ontology TBox into a directed graph and reducing core reasoning to computation of the transitive closure of the graph. We have implemented the algorithm in the QuOnto reasoner and extensively evaluated it over very large ontologies. Our experiments show that QuOnto outperforms various popular reasoners in classification of OWL 2 QL ontologies.