Bottom-up propositionalization
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This is an author’s accepted version of a conference paper published in Proc 10th International Conference on Inductive Logic Programming: Work-in-Progress Reports. © 2022 The Authors.
Abstract
In this paper, we present a new method for propositionalization that works in a bottom-up, data-driven manner. It is tailored for biochemical databases, where the examples are 2-D descriptions of chemical compounds. The method generates all frequent fragments (i.e., linearly connected atoms) up to a user-specified length. A preliminary experiment in the domain of carcinogenicity predictions showed that bottom-up propositionalization is a promising approach to feature construction from relational data.
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CEUR Workshop Proceedings