Declarative Data Generation with ProbLog

Anton Dries
2015 Proceedings of the Sixth International Symposium on Information and Communication Technology - SoICT 2015  
In this paper we describe a novel declarative approach to data generation based on probabilistic logic programming. We show that many data generation tasks can be described as a probabilistic logic program. To this end, we extend the ProbLog language with continuous distributions and we develop a simple sampling algorithm for this language. We demonstrate that many data generation tasks can be described as a model in this language and we provide examples of generators for attribute-value data,
more » ... equences, graphs and logical interpretations and we show how to model common extensions such as noise, missing values and concept drift. • It is modular. It allows different components to be defined separately and be reused.
doi:10.1145/2833258.2833267 dblp:conf/soict/Dries15 fatcat:2nbf6cyx4rf4pidnyqwsbgkj3m