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.

翻译：由于大部分科学文献数据未标注，基于无监督图的语义表示学习变得至关重要。同时，为丰富科学文献的特征，提出一种基于自适应特征与图神经网络的科学文献语义表示学习方法。通过引入自适应特征方法，从全局和局部两个层面考量科学文献特征。利用图注意力机制对具有引用关系的科学文献特征进行求和，并为每篇科学文献赋予不同的特征权重，从而更好地表达不同科学文献特征间的相关性。此外，提出一种无监督图神经网络语义表示学习方法。通过对比潜在空间中科学文献正负局部语义表示与全局图语义表示之间的互信息，图神经网络能够捕获局部与全局信息，从而提升科学文献语义表示的学习能力。实验结果表明，所提出的基于自适应特征与图神经网络的科学文献语义表示学习方法在科学文献分类任务中具有竞争力，并取得了良好效果。