A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Approximate Assertional Reasoning Over Expressive Ontologies

In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed

09411 Abstracts Collection -- Interaction versus Automation: The two Faces of Deduction

From 04.10. to 09.10.2009, the Dagstuhl Seminar 09411 ``Interaction versus Automation: The two Faces of Deduction\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Framework for Parallelizing OWL Classification in Description Logic Reasoners

The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. This thesis reports on a black-box approach to parallelize existing description logic (DL) reasoners for theWeb Ontology Language. We focus on OWL ontology classification, which is an important inference service and supported by every major OWL/DL reasoner. To the best of our knowledge, we are the first to propose a flexible parallel framework which can be applied to existing OWL reasoners in order to speed up their classification process. There are two versions of our methods discussed: (i) the first version implements a novel thread-level parallel architecture with two parallel strategies to achieve a good speedup factor with an increasing number of threads, but does not rely on locking techniques and thus avoids possible race conditions. (ii) The improved version implements an improved data structure and various parallel computing techniques for precomputing and classification to reduce the overhead of processing ontologies and compete with other DL reasoners based on the wall clock time for classification. In order to test the performance of both versions of our approaches, we use a real-world repository for choosing the tested ontologies. For the first version of our approach, we evaluated our prototype implementation with a set of selected real-world ontologies. Our experiments demonstrate very good scalability resulting in a speedup that is linear to the number of available cores. For the second version, its performance is evaluated by parallelizing major OWL reasoners for concept classification. Currently, we mainly focus on comparison with two popular DL reasoners: Hermit and JFact. In comparison to the selected black-box reasoners, our results demonstrate that the wall clock time of ontology classification can be improved by one order of the magnitude for most real-world ontologies in the repository

On the Computation of Common Subsumers in Description Logics

Description logics (DL) knowledge bases are often build by users with expertise in the application domain, but little expertise in logic. To support this kind of users when building their knowledge bases a number of extension methods have been proposed to provide the user with concept descriptions as a starting point for new concept definitions. The inference service central to several of these approaches is the computation of (least) common subsumers of concept descriptions. In case disjunction of concepts can be expressed in the DL under consideration, the least common subsumer (lcs) is just the disjunction of the input concepts. Such a trivial lcs is of little use as a starting point for a new concept definition to be edited by the user. To address this problem we propose two approaches to obtain "meaningful" common subsumers in the presence of disjunction tailored to two different methods to extend DL knowledge bases. More precisely, we devise computation methods for the approximation-based approach and the customization of DL knowledge bases, extend these methods to DLs with number restrictions and discuss their efficient implementation

Kiel Declarative Programming Days 2013

This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems