Technology opportunity analysis using hierarchical semantic networks and dual link prediction

Abstract

Technology opportunity analysis using network analysis and link prediction has attracted the interest of both academia and industry. However, there are several unresolved issues with existing research, such as a lack of semantic relationships between nodes in a single-layer network, analyzing current technology trends rather than predicting future technology developments based on existing edges in a semantic network, and ignoring evaluation criteria for technology opportunity identification based on a single link prediction. This study proposes a new systematic methodology to address these issues and identify technology opportunities using a hierarchical semantic network and dual link prediction. The proposed methodology consists of three modules: 1) constructing the hierarchical semantic network based on SAO structures extracted from patents; 2) identifying technology opportunities in this semantic network through probabilistic-based link prediction; and 3) evaluating these opportunities via similarity-based link prediction. The viability and usefulness of the proposed methodology is proved by empirical analysis of the exploitation technology in the coal seam gas (CSG) industry. The results show that the hierarchical semantic network, including semantic and co-word relationships, can improve prediction accuracy. The dual link prediction can not only automatically identify technology opportunities with semantics, but also evaluate them to narrow down the problem-solving according to the novelty criteria. This study represents a contribution to existing research on technology opportunity analysis by integrating the hierarchical semantic network and dual link prediction.