基于知识图谱与强化学习的专利交易推荐研究  

Research on patent transaction recommendation based on knowledge graph and reinforcement learning

作　　者：何喜军[1] 石安杰 吴爽爽 武玉英[1] HE Xijun;SHI Anjie;WU Shuangshuang;WU Yuying(College of Economics and Management,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学经济与管理学院,北京100124

出　　处：《系统工程理论与实践》2024年第10期3330-3345,共16页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金面上项目(71974009);国家自然科学基金(71774010);国际科研合作基金(2021B35);国家资助博士后研究人员计划C档(GZC20230184)。

摘　　要：筛选影响专利交易的多维特征,构建专利供需知识图谱PSD-KG,引入强化学习并构建专利交易推荐模型KG-RL-PTR,基于实体相似关系及组织历史交易信息设计奖励函数,指导智能体在PSD-KG上根据组织所处环境推理出有效路径,以寻找潜在优质专利完成交易推荐.通过燃料电池领域专利数据的实证研究,得出:第一,与Ekar、DDPG、DeepPath等方法对比,KG-RL-PTR模型推荐性能表现最优;第二,组织间技术邻近是专利交易推荐的关键路径,社会邻近、地理邻近及制度邻近等路径对交易推荐也有促进作用;第三,多维邻近关系路径交互作用共同影响专利交易关系的形成,且技术邻近和组织间信任在推荐中扮演重要角色.该模型依据推理路径给出推荐结果,推荐结果具有新颖性及可解释性.Patent transactions play an important role in improving technological innovation capacity of the demand side and the radiation capacity of the supply side.However,the number of patents in China's technology market is large,but the transaction rate between patents is still at a low level.Therefore,exploring paths of promoting patent transaction rate receives plenty of attention.In this paper,we select multidimensional factors affecting patent transactions based on former studies.Then,the semantic and non-semantic entities in patent information,as well as the multi-dimensional relationships between the entities,are used to construct the patent supply and demand knowledge graph(PSD-KG).Meanwhile,TransE model is used for knowledge graph embedding representation.On this basis,reinforcement learning is introduced to construct a patent transaction recommendation model based on knowledge graph and reinforcement learning(KG-RL-PTR).Then,the reward functions are designed based on the entity similarity relationship and the organization's historical transaction information,which guides the agent to reason out the effective paths based on the environment in which the organization is located on the PSD-KG,so as to search for potential high-quality patents and complete the transaction recommendation.An empirical study is conducted based on patent data in the field of fuel cells.We find that:1) The KG-RL-PTR model has the most superior recommendation performance when compared with Ekar,DDPG,DeepPath and other methods.2) Inter-organizational technological proximity is the key path for patent transaction recommendation.Meanwhile,the paths based on social proximity,geographic proximity,and institutional proximity also contribute to the recommendation process.3) The interaction between multidimensional proximities jointly affects the formation of patent transaction relationships.Among them,technological proximity and social proximity play dominant roles,which suggests that similarity between technologies and interorganizational trus

关 键 词：专利供需知识图谱 强化学习 专利交易推荐 燃料电池 

分 类 号：G306[文化科学]