技术知识产权的自适应双通道卷积超图表示学习
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property
摘要：
在大数据时代，离散国家对技术知识产权中隐藏信息挖掘的需求越来越大。当然，已经提出了相当数量的技术知识产权的图学习算法。其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，并利用神经网络算法提取图中隐藏的结构信息。然而，现有的图学习算法大多只关注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阶信息。为此，提出了一种基于双通道卷积的超图神经网络模型。对于由技术知识产权数据构建的超图，利用超图的超图通道和线扩展图通道学习超图，并引入注意力机制自适应融合两个通道的输出表示。该模型在各种数据集上的性能优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
