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Stratified knowledge bases as interpretable probabilistic models

Kuzelka, Ondrej, Davis, Jesse and Schockaert, Steven 2016. Stratified knowledge bases as interpretable probabilistic models. Presented at: Interpretable ML for Complex Systems NIPS 2016 Workshop, Barcelona, Spain, 9 December 2016.

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Abstract

In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained in existing frameworks such as Markov logic networks. Among others, this allows for the use of domain experts to improve learned models by directly removing, adding, or modifying logical formulas.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date of First Compliant Deposit: 15 December 2016
Last Modified: 07 Aug 2019 10:35
URI: http://orca-mwe.cf.ac.uk/id/eprint/96945

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