TY - JOUR
T1 - Technology opportunity analysis
T2 - Combining sao networks and link prediction
AU - Han, Xiaotong
AU - Zhu, Donghua
AU - Wang, Xuefeng
AU - Li, Jia
AU - Qiao, Yali
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - 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.
AB - 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.
KW - Link prediction
KW - Network analysis
KW - Skin malignant melanoma
KW - Subject-action-object (SAO) network
KW - Technology opportunity analysis
UR - https://www.scopus.com/pages/publications/85095749302
U2 - 10.1109/TEM.2019.2939175
DO - 10.1109/TEM.2019.2939175
M3 - Article
AN - SCOPUS:85095749302
SN - 0018-9391
VL - 68
SP - 1288
EP - 1298
JO - IEEE Transactions on Engineering Management
JF - IEEE Transactions on Engineering Management
IS - 5
M1 - 08846579
ER -