Abstract
Collective inference is a popular approach for solving tasks as knowledge graph completion within the
statistical relational learning field. There are many
existing solutions for this task, however, each of
them is subjected to some limitation, either by restriction to only some learning settings, lacking interpretability of the model or theoretical test error
bounds. We propose an approach based on cautious
inference process which uses first-order rules and
provides PAC-style bounds