Abstract
OWL 2 EL, which is underpinned by the description logic \(\mathcal {EL}\), has been used to build terminological ontologies in real applications, like biomedicine, multimedia and transportation. On the other hand, there have been techniques that allow developers and users acquiring large scale ontologies by automatically extracting data from different sources or integrating different domain ontologies. Thus the issue of handling large scale ontologies has to be tackled. In this short paper, we report our work on classification of OWL 2 EL ontologies using MapReduce, which is a distributed computing model for data processing. We discuss the main problems when we use MapReduce to handle OWL 2 EL classification and how we address these problems. We implement the algorithm using Hadoop, and evaluate it on a cluster of machines. The experimental results show that our prototype system achieves a linear scalability on large scale ontologies.
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Notes
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Amdahl argues in [1] that the speedup s of a computation task depends on the fraction of sequential computation part, i.e., \(s\le 1/(f+\frac{1-f}{p})\), where s is the speedup, f is the fraction of sequential computation part and p is the number of processors.
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This conclusion also holds with a polynomial number of processors, since the classification on EL ontologies is in P.
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Zhou, Z., Qi, G., Liu, C., Mutharaju, R., Hitzler, P. (2016). Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_40
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DOI: https://doi.org/10.1007/978-3-319-45817-5_40
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