Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Parametric Learning of Gaifman Models   We consider the problem of structure learning for Gaifman models and learn
relational features that can be used to derive feature representations from a
knowledge base. These relational features are first-order rules that are then
partially grounded and counted over local neighborhoods of a Gaifman model to
obtain the feature representations. We propose a method for learning these
relational features for a Gaifman model by using relational tree distances. Our
empirical evaluation on real data sets demonstrates the superiority of our
approach over classical rule-learning.

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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph