Semantic Web Reasoning by Swarm Intelligence

Abstract. Semantic Web reasoning systems are confronted with the task to process growing amounts of distributed, dynamic resources. This paper presents a novel way of approaching the challenge by RDF graph traversal, exploiting the advantages of swarm intelligence. The natureinspired and index-free methodology is realised by self-organising swarms of autonomous, light-weight entities that traverse RDF graphs by following paths, aiming to instantiate pattern-based inference rules. The method is evaluated on the basis of a series of simulation experiments with regard to desirable properties of Semantic Web reasoning, focussing on anytime behaviour, adaptiveness and scalability.

Towards intelligent distributed computing : cell-oriented computing

Distributed computing systems are of huge importance in a number of recently established and future functions in computer science. For example, they are vital to banking applications, communication of electronic systems, air traffic control, manufacturing automation, biomedical operation works, space monitoring systems and robotics information systems. As the nature of computing comes to be increasingly directed towards intelligence and autonomy, intelligent computations will be the key for all future applications. Intelligent distributed computing will become the base for the growth of an innovative generation of intelligent distributed systems. Nowadays, research centres require the development of architectures of intelligent and collaborated systems; these systems must be capable of solving problems by themselves to save processing time and reduce costs. Building an intelligent style of distributed computing that controls the whole distributed system requires communications that must be based on a completely consistent system. The model of the ideal system to be adopted in building an intelligent distributed computing structure is the human body system, specifically the body’s cells. As an artificial and virtual simulation of the high degree of intelligence that controls the body’s cells, this chapter proposes a Cell-Oriented Computing model as a solution to accomplish the desired Intelligent Distributed Computing system

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

Scaling Up Description Logic Reasoning by Distributed Resolution

Benefits from structured knowledge representation have motivated the creation of large description logic ontologies. For accessing implicit information and avoiding errors in ontologies, reasoning services are necessary. However, the available reasoning methods suffer from scalability problems as the size of ontologies keeps growing. This thesis investigates a distributed reasoning method that improves scalability by splitting a reasoning process into a set of largely independent subprocesses. In contrast to most description logic reasoners, the proposed approach is based on resolution calculi. We prove that the method is sound and complete for first order logic and different description logic subsets. Evaluation of the implementation shows a heavy decrease of runtime compared to reasoning on a single machine. Hence, the increased computation power pays off the overhead caused by distribution. Dependencies between subprocesses can be kept low enough to allow efficient distribution. Furthermore, we investigate and compare different algorithms for computing the distribution of axioms and provide an optimization of the distributed reasoning method that improves workload balance in a dynamic setting

Forschungsbericht Universität Mannheim 2008 / 2009

Die Universität Mannheim hat seit ihrer Entstehung ein spezifisches Forschungsprofil, welches sich in ihrer Entwicklung und derz eitigen Struktur deutlich widerspiegelt. Es ist geprägt von national und international sehr anerkannten Wirtschafts- und Sozialwissenschaften und deren Vernetzung mit leistungsstarken Geisteswissenschaften, Rechtswissenschaft sowie Mathematik und Informatik. Die Universität Mannheim wird auch in Zukunft einerseits die Forschungsschwerpunkte in den Wirtschafts- und Sozialwissenschaften fördern und andererseits eine interdisziplinäre Kultur im Zusammenspiel aller Fächer der Universität anstreben

Scalable discovery of networked data : Algorithms, Infrastructure, Applications

Harmelen, F.A.H. van [Promotor]Siebes, R.M. [Copromotor

Distributed Resolution for ALC

Abstract. The use of Description Logic as the basis for Semantic Web Languages has led to new requirements with respect to scalable and nonstandard reasoning. In this paper, we address the problem of scalable reasoning by proposing a distributed, complete and terminating algorithm that decides satisfiability of terminologies in ALC. The algorithm is based on recent results on applying resolution to description logics. We show that the resolution procedure proposed by Tammet can be distributed amongst multiple resolution solvers by assigning unique sets of literals to individual solvers. This results provides the basis for a highly scalable reasoning infrastructure for Description logics.

Distributed Resolution for ALC - First Results

Abstract. The use of description logic as the basis for semantic web languages has led to new requirements with respect to scalable and non-standard reasoning. Description logic is a decidable fragment of FOL but still, the standard reasoning tasks are of exponential complexity, satisfiability and subsumption tests are often intractable on large ontologies. Existing large ontologies have a modular structure like networks of linked ontologies, caused by the development process. However, current reasoning approaches do scarcely take advantage of this structure. The available reasoners do not exploit parallel computation and scalability improvements enabled by distributed reasoning. In this paper, we lay the foundation for developing distributed reasoning methods by showing that the description logic fragment ALC can be distributed. We propose a distributed, complete and terminating algorithm that decides satisfiability of terminologies in ALC. The algorithm is based on recent results on applying resolution to description logics. We show that the resolution procedure proposed by Tammet can be distributed amongst multiple resolution solvers by assigning unique sets of literals to individual solvers. This provides the basis for a highly scalable reasoning infrastructure for description logics.