kProbLog: an algebraic Prolog for kernel programming. kProbLog is a simple algebraic extension of Prolog with facts and rules annotated with semiring labels. We propose kProbLog as a language for learning with kernels. kProbLog allows to elegantly specify systems of algebraic expressions on databases. We propose some code examples of gradually increasing complexity, we give a declarative specification of some matrix operations and an algorithm to solve linear systems. Finally we show the encodings of state-of-the-art graph kernels such as Weisfeiler-Lehman graph kernels, propagation kernels and an instance of Graph Invariant Kernels (GIKs), a recent framework for graph kernels with continuous attributes. The number of feature extraction schemas, that we can compactly specify in kProbLog, shows its potential for machine learning applications.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Azzolini, Damiano; Riguzzi, Fabrizio: Optimizing probabilities in probabilistic logic programs (2021)
- Šourek, Gustav; Železný, Filip; Kuželka, Ondřej: Beyond graph neural networks with lifted relational neural networks (2021)
- Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
- Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
- Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)