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基于图卷积网络的发明人跨领域合作伙伴识别方法

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[研究目的]科学技术与社会的发展促进了不同领域理论、方法和技术的交叉融合,跨领域合作愈发成为合作创新的主流形式,如何帮助发明人定位并准确识别跨领域合作伙伴成为亟待解决的问题.[研究方法]提出一种基于图卷积网络的发明人跨领域合作伙伴识别方法,从多维特征视角下基于发明人专利信息中的合作关系特征、摘要文本特征、领域信息特征使用图卷积网络识别和预测发明人潜在合作伙伴,构建同领域指数和跨领域指数准确识别发明人跨领域合作伙伴.[研究结论]通过对比实验,证明了借助图卷积网络对合作关系特征、摘要文本特征、领域信息特征三维特征联用在进行伙伴识别时能够有效提升模型准确性.借助识别跨领域合作伙伴,有助于促进不同领域之间的交叉合作和知识转移,创造出更具创新性和前瞻性的成果.
Research on the Method of Identifying Cross-Domain Collaborative Partners for Inventors Based on Graph Convolutional Networks
[Research purpose]The development of science,technology,and society has driven the integration of theories,methods,and techniques across diverse fields.Cross-disciplinary collaboration is increasingly becoming the predominant form of cooperative innovation.Identifying and accurately pinpointing potential partners for such collaborations has emerged as a pressing challenge.[Research method]This paper introduces a novel method for the identification of cross-domain collaboration partners of inventors utilizing Graph Convolution-al Networks(GCNs).By adopting a multi-dimensional feature perspective,we harnessed features from cooperation relationships,abstract textual content,and domain-specific data inherent within inventor patent records.Through the application of GCNs,we were able to sys-tematically identify and predict potential collaborators for inventors,strategically developing both intra-domain and inter-domain indices for precise identification of cross-domain collaborative partnerships.[Research conclusion]Through comparative experiments,it has been demonstrated that leveraging the GCNs in conjunction with three-dimensional features—collaboration relationship,field information,and abstract textual features—can significantly enhance model accuracy.Identifying cross-disciplinary collaborators fosters cross-field col-laborations and knowledge transfer,leading to more innovative and forward-looking outcomes.

inventorpatent informationmultidimensional featuresgraph convolutional networklink predictioncross-domain indexscientific collaborationcollaborative partners

谢小东、吴洁、盛永祥、王建刚、周潇

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江苏科技大学经济管理学院 镇江 212003

发明人 专利信息 多维特征 图卷积网络 链路预测 跨领域指数 科研合作 合作伙伴

国家社会科学基金后期资助项目国家自然科学基金面上项目江苏省研究生科研与实践创新计划

19FGLB02972171122KYCX23_3817

2024

10.3969/j.issn.1002-1965.2024.04.023

情报杂志
陕西省科学技术信息研究所

情报杂志

CSTPCDCSSCICHSSCD北大核心
影响因子：1.502
ISSN：1002-1965
年,卷(期)：2024.43(4)
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