PatSTEG：通过语义-拓扑演化图建模专利引用网络的形成动态
PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph
AI总结：
提出 PatSTEG 联合语义-拓扑演化图学习方法，建模专利引用网络形成动态，在 CNPat 和公开数据集上验证其优于现有方法。
摘要：
专利数据库（PatDB）中的专利文献对于研究、开发和创新至关重要，因为它们包含有价值的技术信息。然而，与公开可用的预处理数据库相比，PatDB 由于专利文本的复杂性和专利引用网络固有的稀疏性，呈现出多方面的挑战。尽管专利文本分析和引文分析为探索专利数据挖掘带来了新的机遇，但现有工作均未利用它们之间的互补性。为此，我们提出了一种联合语义-拓扑演化图学习方法（PatSTEG），用于建模专利引用网络的形成动态。具体而言，我们首先创建了一个名为 CNPat 的真实世界中国专利数据集，并利用其专利文本和引文构建专利引用网络。然后，对 PatSTEG 进行建模，通过联合考虑语义和拓扑信息来研究专利引用形成的演化动态。我们在 CNPat 和公开数据集上进行了大量实验，以证明 PatSTEG 相对于其他最先进方法的优越性。所有结果为专利文献研究和技术探索提供了有价值的参考。
英文摘要：
Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information. However, PatDB presents a multifaceted challenge compared to publicly available preprocessed databases due to the intricate nature of the patent text and the inherent sparsity within the patent citation network. Although patent text analysis and citation analysis bring new opportunities to explore patent data mining, no existing work exploits the complementation of them. To this end, we propose a joint semantic-topological evolutionary graph learning approach (PatSTEG) to model the formation dynamics of patent citation networks. More specifically, we first create a real-world dataset of Chinese patents named CNPat and leverage its patent texts and citations to construct a patent citation network. Then, PatSTEG is modeled to study the evolutionary dynamics of patent citation formation by considering the semantic and topological information jointly. Extensive experiments are conducted on CNPat and public datasets to prove the superiority of PatSTEG over other state-of-the-art methods. All the results provide valuable references for patent literature research and technical exploration.
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。
