论文标题
自适应双渠道卷积超图表的技术知识产权学习
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property
论文作者
论文摘要
在大数据时代，在离散国家 /地区，对技术知识产权中隐藏信息挖掘的需求正在增加。毫无疑问，已经提出了大量用于技术知识产权的图形学习算法。目标是通过图结构对技术知识产权实体及其关系进行建模，并使用神经网络算法来提取图中的隐藏结构信息。但是，大多数现有的图形学习算法仅着眼于技术知识产权中二元关系的信息挖掘，而忽略了隐藏在非二元关系中的高级订单信息。因此，提出了基于双向通道卷积的高图神经网络模型。对于从技术知识产权数据构建的超电器，使用HyperGraph通道和线扩展的图形通道用于学习HyperGraph，并且引入了注意机制以适应两个通道的输出表示形式。所提出的模型优于各种数据集上现有方法的表现。
In the age of big data, the demand for hidden information mining in technological intellectual property is increasing in discrete countries. Definitely, a considerable number of graph learning algorithms for technological intellectual property have been proposed. The goal is to model the technological intellectual property entities and their relationships through the graph structure and use the neural network algorithm to extract the hidden structure information in the graph. However, most of the existing graph learning algorithms merely focus on the information mining of binary relations in technological intellectual property, ignoring the higherorder information hidden in non-binary relations. Therefore, a hypergraph neural network model based on dual channel convolution is proposed. For the hypergraph constructed from technological intellectual property data, the hypergraph channel and the line expanded graph channel of the hypergraph are used to learn the hypergraph, and the attention mechanism is introduced to adaptively fuse the output representations of the two channels. The proposed model outperforms the existing approaches on a variety of datasets.
