Abstract
Support for modularity allows complex ontologies to be separated into smaller pieces (modules) that are easier to maintain and compute. Instead of considering the entire complex ontology, users may benefit more by starting from a problem-specific set of concepts (signature of problem) from the ontology and exploring its surrounding logical modules. Additionally, an ontology modularization mechanism allows for the splitting up of ontologies into modules that can be processed by isolated separate instances of the inference engine. The relational algorithm, described in this paper, makes it possible to construct an inference engine that can run in a highly scalable cloud computing environment, or on a computer grid.
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Kaplanski, P. (2009). Syntactic Modular Decomposition of Large Ontologies with Relational Database. In: Nguyen, N.T., Katarzyniak, R.P., Janiak, A. (eds) New Challenges in Computational Collective Intelligence. Studies in Computational Intelligence, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03958-4_6
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DOI: https://doi.org/10.1007/978-3-642-03958-4_6
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