Definition
Propositionalization is the process of explicitly transforming a Relational dataset into a propositional dataset.
The input data consists of examples represented by structured terms (cf. Learning from Structured Data), several predicates in First-Order Logic, or several tables in a relational database. We jointly refer to these as relational representations. The output is an Attribute-value representation in a single table, where each example corresponds to one row and is described by its values for a fixed set of attributes. New attributes are often called features to emphasize that they are built from the original attributes. The aim of propositionalization is to pre-process relational data for subsequent analysis by attribute-value learners. There are several reasons for doing this, the most important of which are: to reduce the complexity and speed up the learning; to separate modeling the data from hypothesis construction; or to use familiar attribute-value (or...
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Lachiche, N. (2011). Propositionalization. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_680
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DOI: https://doi.org/10.1007/978-0-387-30164-8_680
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