Abstract
In many real situations it is not possible to merge multiple knowledge bases into a single one using one-level integration. It could be caused, for example, by high complexity of the integration process or geographical distance between servers that host knowledge bases that expected to be integrated. The paralleling of integration process could solve this problem and in this paper we propose a multi-level ontology integration procedure. The analytical analysis pointed out that for presented algorithm the one- and multi-level integration processes give the same results (the same final ontology). However, the multi-level integration allows to save time of data processing. The experimental research demonstrated a significant difference between times required for the one- and multi-level integration procedure. The latter could be even 20 \(\%\) faster than the former, which is important especially in the emerging context of Big Data. Due to the limited space we can only consider integration on the concept level.
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Acknowledgment
This work was partially supported by the European Commission under the 7th Framework Programme, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European research centre of Network intelliGence for INnovation Enhancement (http://engine.pwr.wroc.pl/).
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Kozierkiewicz-Hetmańska, A., Pietranik, M. (2016). Preliminary Evaluation of Multilevel Ontology Integration on the Concept Level. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_7
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DOI: https://doi.org/10.1007/978-3-662-49381-6_7
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