Technology Opportunity Analysis: Combining SAO Networks and Link Prediction

• Published in: IEEE transactions on engineering management Vol. 68; no. 5; pp. 1288 - 1298
• Main Authors: Han, Xiaotong, Zhu, Donghua, Wang, Xuefeng, Li, Jia, Qiao, Yali
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cancer; Link prediction; Melanoma; Network analysis; New technology; Prediction algorithms; Semantic analysis; Semantics; skin malignant melanoma; subject–action–object (SAO) network; Tech mining; Technological innovation; Technology assessment; technology opportunity analysis; Text mining
• ISSN: 0018-9391; 1558-0040
• DOI: 10.1109/TEM.2019.2939175

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.