Particle Swarm Optimisation (PSO) is a biologically-inspired, population-based optimisation technique that has been successfully applied to various problems in science and engineering. In the context of semantic technologies, optimisation problems also occur but have rarely been considered as such. This work addresses the problem of ontology alignment, which is the identification of overlaps in heterogeneous knowledge bases backing semantic applications. To this end, the ontology alignment problem is revisited as an optimisation problem. A discrete particle swarm optimisation algorithm is designed in order to solve this optimisation problem and compute an alignment of two ontologies. A number of characteristics of traditional PSO algorithms are partially relaxed in this article, such as fixed dimensionality of particles. A complex fitness function based on similarity measures of ontological entities, as well as a tailored particle update procedure are presented. This approach brings several benefits for solving the ontology alignment problem, such as inherent parallelisation, anytime behaviour, and flexibility according to the characteristics of particular ontologies. The presented algorithm has been implemented under the name MapPSO (Ontology Mapping using Particle Swarm Optimisation). Experiments demonstrate that applying PSO in the context of ontology alignment is a feasible approach.
Abstract. As current reasoning techniques are not designed for massive parallelisation, usage of parallel computation techniques in reasoning establishes a major research problem. I will propose two possibilities of applying parallel computation techniques to ontology reasoning: parallel processing of independent ontological modules, and tailoring the reasoning algorithms to parallel architectures. MotivationScalability is an issue that is subject in many semantic web research discussions. More and more researchers share the awareness that reasoning in its current form will not be able to bear the load of data it is supposed to handle in the near future. A polarising article was published by Fensel and van Harmelen [1], where the authors talk about "10.000 triples just to describe each human, which gives us 100 trillion." Even though this guess may be intensionally provocative, it has in fact been proven several times in the past that even high estimations of growth were beaten in reality often long before they were predicted to eventuate.An oberervation is, that available state-of-the-art reasoners do not exploit the benefits of parallel computation techniques, as these are not straightforwardly applied for reasoning calculi. Multithreading or other ways of distributed computation cannot easily be taken care of by the operating system. This is a major problem, since computational power at the level of integrated circuits is about to explore its physical limits by being "down to atoms" concerning conductor and transistor size. However, parallel computer architectures emerge, such as grid, peer-to-peer, or multi-core machines even in home-computing environments. This allows for the overall computational power to grow further, provided that software architecture and algorithms respect this computational paradigm shift. Thus parallel architectures do not inherently speed up all kind of computation, as the workload needs to be split into chunks of independent computations. Current reasoning algorithms do not naturally decompose into independent computational chunks.In fact, it remains an open question whether algorithms currently used in reasoning adapt well to the paradigm shift in computer architecture. In particular it is unclear whether well established tableau algorithms, as widely used in stateof-the-art reasoners, can be parallelised.
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