Reasoning about Unmodelled Concepts - Incorporating Class Taxonomies in
  Probabilistic Relational Models
  A key problem in the application of first-order probabilistic methods is the
enormous size of graphical models they imply. The size results from the
possible worlds that can be generated by a domain of objects and relations. One
of the reasons for this explosion is that so far the approaches do not
sufficiently exploit the structure and similarity of possible worlds in order
to encode the models more compactly. We propose fuzzy inference in Markov logic
networks, which enables the use of taxonomic knowledge as a source of imposing
structure onto possible worlds. We show that by exploiting this structure,
probability distributions can be represented more compactly and that the
reasoning systems become capable of reasoning about concepts not contained in
the probabilistic knowledge base.
