Abstract
Data contain given facts, which are explicitly expressed. If we have the facts that Nils is a child of Sven and Sven is a child of Josef, then we as humans know that Josef is the grandparent of Nils, which is also called implicit knowledge. However, machines cannot process implicit knowledge as humans can do. Machines must get to know how to transform implicit knowledge to explicit knowledge, that is, to facts, such that machine can process it. The transformation from implicit knowledge to explicit knowledge is often expressed by rules. The application of rules to determine new facts is called inference. Inference is a costly operation, often leading to higher costs than query processing. We propose different materialization strategies for inferred facts to optimize query processing on inferred facts in this chapter and examine their performance gains.
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Groppe, S. (2011). Inference. In: Data Management and Query Processing in Semantic Web Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19357-6_9
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DOI: https://doi.org/10.1007/978-3-642-19357-6_9
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