多维特征视角下基于图卷积网络的专利技术领域自动识别研究

专利审查周期缩短政策的提出与专利申请数量急剧增加的现状给实现专利技术领域识别的专利分类工作带来巨大挑战，如何引入专利自动分类技术提高专利分类工作效率、缩短专利审查周期成为重要研究主题。本文提出基于多维特征和图卷积网络的专利技术领域自动识别方法。该方法根据文献计量学与图表示学习理论从专利摘要、引证专利、专利发明人维度提取专利特征；其次利用专利摘要维度特征生成表征专利文本特征的专利-核心词汇异构网络，并将引证专利、专利发明人维度特征作为专利数字特征嵌入专利-核心词汇异构网络；通过图卷积网络进行半监督学习，确定专利-核心词汇异构网络中专利节点的类别标签，完成专利自动分类任务。为验证本文所提方法的识别效果，采用Incopat全球专利数据库中专利数据进行实验；实验结果表明专利文本特征与专利数字特征共同作为专利特征可以提高专利分类准确率，引证专利信息的引入可以提高专利分类准确率。同时，本文所提方法也给专利技术领域自动识别问题提供新解答思路，为缩短专利审查周期政策的实施提供支撑。

Patent Classification Based on multi-dimensional Feature and Graph Convolutional Networks

The shortening of patent examination time and the increase of patent number bring great challenges to patent classification, and using patent automatic classification technology to improve the efficiency of patent classification and shorten the time of patent examination has become an important research topic. An automatic patent classification framework is proposed based on multi-dimensional features and graph convolutional networks. The framework extracts the patent features from the dimensions of patent abstract, citation patent and patent inventor according to document metrology and graph representation learning theory. Secondly, the patent-core word network is constructed by using the dimensionality features of patent abstracts, and the dimensionality features of citation patents and patent inventors are embedded into the patent-core word network as patent number features. The semi-supervised learning of graph convolutional network is used to determine the classification labels of patent nodes in the patent-core word co-occurrence network and complete the task of patent automatic classification. In order to verify the effect of the method, the patent data from the Incopat global patent database are used for experiments. The experimental results show that the patent text information and the patent structured information as the patent features can improve the patent classification accuracy, and the introduction of backward citation patent information can improve the patent classification accuracy. At the same time, the framework proposed in this paper also provides a new solution to the problem of patent automatic classification, and provides support for the implementation of the policy of shortening patent examination time.