Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: KGPool: Dynamic Knowledge Graph Context Selection for Relation
  Extraction   We present a novel method for relation extraction (RE) from a single
sentence, mapping the sentence and two given entities to a canonical fact in a
knowledge graph (KG). Especially in this presumed sentential RE setting, the
context of a single sentence is often sparse. This paper introduces the KGPool
method to address this sparsity, dynamically expanding the context with
additional facts from the KG. It learns the representation of these facts
(entity alias, entity descriptions, etc.) using neural methods, supplementing
the sentential context. Unlike existing methods that statically use all
expanded facts, KGPool conditions this expansion on the sentence. We study the
efficacy of KGPool by evaluating it with different neural models and KGs
(Wikidata and NYT Freebase). Our experimental evaluation on standard datasets
shows that by feeding the KGPool representation into a Graph Neural Network,
the overall method is significantly more accurate than state-of-the-art
methods.

29
The categories of this paper are: funct-an
2
The categories of this paper are: math
19
The categories of this paper are: eess
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom