Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph