融合知识网络嵌入特征的高价值专利预测

Prediction of High-value Patents by Incorporating Knowledge Network Embeddedness

准确预测专利价值,尽早识别具有较高价值的专利对促进高价值专利技术的培育和发展,提前进行技术布局具有重要意义。基于知识重组和专利发明创造过程,划分专利价值特征,通过构建样本专利知识网络和领域先前知识网络,选取和计算知识网络嵌入特征来量化专利的新颖性和常规性,并将其与创新主体及专利申请特征加以融合,构建用于专利价值早期预测的指标体系,利用机器学习算法对处于申请早期的专利价值进行预测。以神经网络技术领域的专利进行实证研究。研究结果表明,融合知识网络嵌入特征的高价值专利预测模型F 1值达到80%,预测结果具有有效性,并且知识网络嵌入特征尤其是网页排名(PageRank)和特征向量中心性等对预测高价值专利具有重要作用。

Accurate prediction of patent value and early identification of patents with high value are of great significance to promote the cultivation of high-value patents and technical layout.Based on knowledge reorganization and patent invention creation process,the article selects and designs indicators of the knowledge network embeddedness to represent the association between sample patents′knowledge and the prior knowledge of their domain.By having integrated the characteristics of innovation actors and patent application,a variety of machine learning models are built to predict the value of the patents at early stage of their application.The high-value patents in the field of neural networks are studied empirically,and the F1 value of the proposed high-value patent prediction model reaches 80%,and the prediction results are effective.Meanwhile,knowledge network embeddedness(especially PageRank and eigenvector centrality)plays an important role in predicting high-value patents.