Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Network Fragments: Representing Knowledge for Constructing Probabilistic
  Models   In most current applications of belief networks, domain knowledge is
represented by a single belief network that applies to all problem instances in
the domain. In more complex domains, problem-specific models must be
constructed from a knowledge base encoding probabilistic relationships in the
domain. Most work in knowledge-based model construction takes the rule as the
basic unit of knowledge. We present a knowledge representation framework that
permits the knowledge base designer to specify knowledge in larger semantically
meaningful units which we call network fragments. Our framework provides for
representation of asymmetric independence and canonical intercausal
interaction. We discuss the combination of network fragments to form
problem-specific models to reason about particular problem instances. The
framework is illustrated using examples from the domain of military situation
awareness.

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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc