Technology Opportunity Analysis: Combining SAO Networks and Link Prediction
Technology Opportunity Analysis: Combining SAO Networks and Link Prediction
复制标题
技术机会分析：SAO 网络与链路预测的结合
DOI：
10.1109/tem.2019.2939175
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发表时间：
2021-10-01
影响因子：
5.8
通讯作者：
Qiao, Yali
中科院分区：
文献类型：
--
作者：
Han, Xiaotong;Zhu, Donghua;Qiao, Yali
发现技术环境变化的第一个迹象是企业成功的关键因素，技术机会分析可以是识别这些迹象的关键过程。然而，常见的基于关键字的分析方法并不能完全表达技术之间的关系。主体-操作-对象（SAO）分析为这个问题提供了一个解决方案，但是目前，这些方法只考虑已经存在的关系。然而，从直觉上看，技术机会最有可能存在于潜在的联系中。为了验证这一观点，在本文中，我们对皮肤恶性黑色素瘤进行了案例研究。首先，我们构建了医学文献标题和摘要的SAO网络，然后使用链接预测算法来识别未连接节点之间可能的未来链接。通过回溯算法进一步分析这些可能的新技术组合，以揭示最有前途的技术机会。结合医学知识对结果进行进一步分析，证实了我们方法的有效性。
Detecting the first signs of change in one's technological surroundings is a critical factor in the success of an enterprise, and technology opportunity analysis can be a crucial process in identifying those signs. However, the common keyword-based methods of analysis do not fully express the relationships between technologies. Subject–action–object (SAO) analysis offers a solution to this problem but, currently, these methods only consider the relationships that already exist. Yet, intuitively, technology opportunities are most likely to reside in potential connections. To test this notion, in this article we conduct a case study on malignant melanoma of the skin. First, we construct an SAO network of the titles and abstracts of medical documents, then use a link prediction algorithm to identify probable future links between unconnected nodes. These possible new technology combinations are further analyzed with a backtracking algorithm to reveal the most promising technology opportunities. Further analysis of the results combined with medical knowledge confirms the effectiveness of our method.