因果知识引导的技术机会发现——以电动汽车充电桩为例

Discovering Technology Opportunities with Causal Knowledge:Case Study of EV Charging Stations

【目的】将因果思想引入技术机会发现,提出从技术文本的因果知识中识别技术机会,并以电动汽车充电桩为例进行实证研究。【方法】提出因果对自动抽取、因果网络构建、技术机会匹配发现三步骤法。首先,利用规则匹配方法,基于因果触发词和规则模板,自动抽取出多源数据中蕴含的因果对,并以三元组结构表征;然后,构建包含技术要素的因果网络;同时,通过情感识别、需求词抽取等步骤发现用户使用过程中的需求因素;最后,通过对因果网络进行链路预测,补全潜在因果关联,并与用户需求因素进行匹配,最终实现技术机会发现。【结果】分析发现,充电桩的电池性能和价格费用分别是提升技术性能和用户满意度的关键因素。通过对比两种算法,结果显示,GraphSAGE算法比Node2Vec算法能更准确预测连边,有效识别充电桩的潜在技术机会。【局限】因果网络的稀疏性导致准确性还有待提高。【结论】所提方法能够促进科学技术的创新机会识别,旨在发现潜在的不确定性问题,为进一步的技术优化和产业升级提供参考。

[Objective]This paper proposes a new method to identify technology opportunities from documents with the help of causal knowledge.[Methods]The proposed method includes three steps of automatic extraction of causal pairs,construction of causal network and discovery of matching tech-opportunities.Firstly,we used the rule matching method to automatically extract the causal pairs from multi-source data based on causal trigger words and rule templates.We also represented these pairs by triple structure.Then,we constructed the causal network including technical elements and found the demand factors in the process of use.Finally,we completed the potential causal correlation with the link prediction of causal network,which was matched with user demand factors and helped us discover tech-opportunities.[Results]We examined the proposed model with charging stations data of the EVs.We found the battery performance and charging costs are the key factors to improve technical performance and user experience.The GraphSAGE algorithm can more accurately predict the edge connection than Node2Vec,which effectively identify the potential technical opportunities.[Limitations]The accuracy of the proposed method needs to be improved.[Conclusions]The proposed method could effectively discover sci-tech innovation opportunities,as well as potential uncertain issues,which provides reference for further technology optimization and industry upgrading.