Abstract
The field of Statistical Relational Learning (SRL) is
concerned with learning probabilistic models from
relational data. Learned SRL models are typically
represented using some kind of weighted logical
formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can
contain many formulas that interact in non-trivial
ways and weights do not always have an intuitive
meaning. To address this, we propose a new SRL
method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly
encode what can be derived with a given level of
certainty. Compared to Markov Logic Networks
(MLNs), our method is faster and produces considerably more interpretable models