SynthLog: A Language for Synthesising Inductive Data Models
Machine Learning and Knowledge Discovery in Databases: International Workshops …, 2020•Springer
We introduce SynthLog, an extension of the probabilistic logic programming language
ProbLog, for synthesising inductive data models. Inductive data models integrate data with
predictive and descriptive models, in a way that is reminiscent of inductive databases.
SynthLog provides primitives for learning and manipulating inductive data models, it
supports data wrangling, learning predictive models and constraints, and probabilistic and
constraint reasoning. It is used as the back-end of the automated data scientist approach …
ProbLog, for synthesising inductive data models. Inductive data models integrate data with
predictive and descriptive models, in a way that is reminiscent of inductive databases.
SynthLog provides primitives for learning and manipulating inductive data models, it
supports data wrangling, learning predictive models and constraints, and probabilistic and
constraint reasoning. It is used as the back-end of the automated data scientist approach …
Abstract
We introduce SynthLog, an extension of the probabilistic logic programming language ProbLog, for synthesising inductive data models. Inductive data models integrate data with predictive and descriptive models, in a way that is reminiscent of inductive databases. SynthLog provides primitives for learning and manipulating inductive data models, it supports data wrangling, learning predictive models and constraints, and probabilistic and constraint reasoning. It is used as the back-end of the automated data scientist approach that is being developed in the SYNTH project. An overview of the SynthLog philosophy and language as well as a non trivial example of its use, is given in this paper.
Springer
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