  Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in
AI-related applications. Despite the large sizes, existing KGs are far from
complete and comprehensive. In order to continuously enrich KGs, automatic
knowledge construction and update mechanisms are usually utilized, which
inevitably bring in plenty of noise. However, most existing knowledge graph
embedding (KGE) methods assume that all the triple facts in KGs are correct,
and project both entities and relations into a low-dimensional space without
considering noise and knowledge conflicts. This will lead to low-quality and
unreliable representations of KGs. To this end, in this paper, we propose a
general multi-task reinforcement learning framework, which can greatly
alleviate the noisy data problem. In our framework, we exploit reinforcement
learning for choosing high-quality knowledge triples while filtering out the
noisy ones. Also, in order to take full advantage of the correlations among
semantically similar relations, the triple selection processes of similar
relations are trained in a collective way with multi-task learning. Moreover,
we extend popular KGE models TransE, DistMult, ConvE and RotatE with the
proposed framework. Finally, the experimental validation shows that our
approach is able to enhance existing KGE models and can provide more robust
representations of KGs in noisy scenarios.
