Semantic Representation Learning of Scientific Literature based on
  Adaptive Feature and Graph Neural Network
Hongrui Gao Yawen Li Meiyu Liang Zeli Guan Zhe Xue
Because most of the scientific literature data is unmarked, it makes semantic
representation learning based on unsupervised graph become crucial. At the same
time, in order to enrich the features of scientific literature, a learning
method of semantic representation of scientific literature based on adaptive
features and graph neural network is proposed. By introducing the adaptive
feature method, the features of scientific literature are considered globally
and locally. The graph attention mechanism is used to sum the features of
scientific literature with citation relationship, and give each scientific
literature different feature weights, so as to better express the correlation
between the features of different scientific literature. In addition, an
unsupervised graph neural network semantic representation learning method is
proposed. By comparing the mutual information between the positive and negative
local semantic representation of scientific literature and the global graph
semantic representation in the potential space, the graph neural network can
capture the local and global information, thus improving the learning ability
of the semantic representation of scientific literature. The experimental
results show that the proposed learning method of semantic representation of
scientific literature based on adaptive feature and graph neural network is
competitive on the basis of scientific literature classification, and has
achieved good results.