arXiv Open Access 2016

Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)

Ondrej Kuzelka Jesse Davis Steven Schockaert

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.AI

Penulis (3)

Ondrej Kuzelka

Jesse Davis

Steven Schockaert

Format Sitasi

APA MLA BibTeX

Kuzelka, O., Davis, J., Schockaert, S. (2016). Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). https://arxiv.org/abs/1611.06174

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓