Abstract
Nowadays there is a large number of Expert Systems available to users requiring the extraction of data relevant to specific domains, many of which are based on reasoning and inference. However, many of these tools offer slow execution time, resulting in delayed response times to the queries made by users. The strategy of caching to define specific patterns of results enables such systems to eliminate the requirement to repeat the same queries, speeding up the response time and eliminating redundancy. This paper proposes a caching strategy for an Expert System based on Semantic Web and reasoning and inference techniques. Caching strategies have previously been applied to simple XML queries. Performance has been evaluated using an existing system for medical diagnosis, which demonstrates the increased efficiency of the system.
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Rodríguez, A. et al. (2009). Applying Caching Capabilities to Inference Applications Based on Semantic Web. 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_3
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DOI: https://doi.org/10.1007/978-3-642-03958-4_3
Publisher Name: Springer, Berlin, Heidelberg
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