摘要
【目的】综合利用专利分类网络结构特征与文本语义特征,基于多种特征形成技术融合关系预测方法和价值评估方法。【方法】区分专利与专利分类间的关联强度,构建专利分类共现网络,获取专利分类间的网络结构相似性特征,并根据关联强度赋予专利分类以专利文本,利用文本表示学习方法得到其文本语义相似性特征。根据网络结构特征和文本语义特征构建专利分类间多种相似性指标,融合多种指标构成特征向量,利用随机森林模型学习不同指标的权重和贡献,计算技术融合概率,排序得到候选技术融合关系集合。基于专利分类引用网络特征和文献计量特征,从影响力和成长潜力出发,提出领域技术价值、商业价值和战略价值评估指标,利用被引数加以验证,最后用所得方法评估技术融合关系,获取高价值技术融合关系。【结果】本文方法的TopK预测准确率比单一特征至少提高20%;评测得到的前10对高价值技术融合关系与真实排名相差极小,平均绝对误差仅为3.2。【局限】选取的数据库存在数据项不统一的问题;只尝试了单一的随机森林方法,未对其他前沿方法进行验证。【结论】专利分类关联强度能够提高网络分析预测方法的预测效果,同时多特征融合方法相较于单一特征预测方法,能够提高技术融合关系预测效果;另一方面,本文的价值评估方法能够有效实现高价值技术融合关系价值的筛选。
[Objective] This paper proposes a new method to predict technology convergence relationship and their values based on the patent classification network and text semantic features. [Methods] First, we calculated the correlation between patents and their classification to construct the co-occurrence network and obtain their structure similarity features. Then, we connected patent texts and their classification schema with the correlation strength. We also obtained the text semantic similarity features using text representation learning. Second, we constructed similarity indicators with the network structure and text semantic features, which were fused to create a feature vector. Third, we used the random forest model to learn the weights and contributions of different indicators and calculated the technology fusion probability. We also generated the candidate technology fusion relationship set. Fourth, based on the network characteristics and bibliometric characteristics of patent classification and citation, as well as their influence and potential growth, we created the evaluation indices for their technical, commercial and strategic values. Finally, we used the proposed method to evaluate the technology integration relationship. [Results] The accuracy of the proposed method is at least 20% higher than that of single feature prediction. In addition, the top 10 pairs of high-value technology convergence relations that identified by the proposed method have little difference with the real ranking result, in which the MAE is only 3.2.[Limitations] Some data sets are in-consistent, while more machine learning methods need to be utilized.[Conclusions] The feature convergence method has higher prediction accuracy than traditional methods. The proposed method can also effectively evaluate technology convergence relationship value.
作者
张金柱
韩永亮
Zhang Jinzhu;Han Yongliang(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第2期33-44,共12页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金面上项目(项目编号:71974095)
江苏省研究生科研与实践创新计划项目(项目编号:SJCX21_0159)的研究成果之一。
关键词
多特征
技术融合关系
预测
价值评估
Multiple Features
Technology Convergence Relationship
Forecast
Value Assessment
