Innovation Hierarchy Based Patent Representation Model

被引：0
|
作者：
Liu, Weidong [1 ,2 ,3 ,4 ,5 ]
Zhang, Haijie [1 ]
机构：
[1] Inner Mongolia Univ, Coll Comp Sci, Hohhot, Peoples R China
[2] Inst Sci & Tech Informat China, Beijing, Peoples R China
[3] Natl & Local Joint Engn Res Ctr Intelligent Infor, Hohhot, Peoples R China
[4] Inner Mongolia Key Lab Social Comp & Data Proc, Hohhot, Peoples R China
[5] Inner Mongolia Engn Lab Big Data Anal Technol, Hohhot, Peoples R China
来源：
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2022年
基金：
美国国家科学基金会;
关键词：
innovations; hierarchy; attention mechanism; nested hierarchical Dirichlet process;
D O I：
10.1109/IJCNN55064.2022.9892513
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Patent representation is a critical task for patent data mining. However, the patent content has depth and layers, which is hard to understand. There are two challenges in patent representation, including: how to identify the patent innovations and demonstrate the innovation hierarchy. To solve above issues, we propose the Innovation Hierarchy Based Patent Representation Model (IHPRM). In this model, we use the attention mechanism to identify innovations and the nested hierarchical Dirichlet process to represent the innovation hierarchy. In this paper, 1,000 patents are used to test the effectiveness of IHPRM. The result shows that IHPRM has better performance than the comparative model.
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页数：8
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