Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rule Mining over Knowledge Graphs via Reinforcement Learning   Knowledge graphs (KGs) are an important source repository for a wide range of
applications and rule mining from KGs recently attracts wide research interest
in the KG-related research community. Many solutions have been proposed for the
rule mining from large-scale KGs, which however are limited in the inefficiency
of rule generation and ineffectiveness of rule evaluation. To solve these
problems, in this paper we propose a generation-then-evaluation rule mining
approach guided by reinforcement learning. Specifically, a two-phased framework
is designed. The first phase aims to train a reinforcement learning agent for
rule generation from KGs, and the second is to utilize the value function of
the agent to guide the step-by-step rule generation. We conduct extensive
experiments on several datasets and the results prove that our rule mining
solution achieves state-of-the-art performance in terms of efficiency and
effectiveness.

29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph