Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Open Knowledge Graphs Canonicalization using Variational Autoencoders   Noun phrases and Relation phrases in open knowledge graphs are not
canonicalized, leading to an explosion of redundant and ambiguous
subject-relation-object triples. Existing approaches to solve this problem take
a two-step approach. First, they generate embedding representations for both
noun and relation phrases, then a clustering algorithm is used to group them
using the embeddings as features. In this work, we propose Canonicalizing Using
Variational Autoencoders (CUVA), a joint model to learn both embeddings and
cluster assignments in an end-to-end approach, which leads to a better vector
representation for the noun and relation phrases. Our evaluation over multiple
benchmarks shows that CUVA outperforms the existing state-of-the-art
approaches. Moreover, we introduce CanonicNell, a novel dataset to evaluate
entity canonicalization systems.

28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an