Scientific Literature based Big Data Analysis for Technology Insight

Abstract

The increasingly massive amount and open access of literature provide a data foundation for technology insight based on big data analysis. This paper proposed a new technology insight framework -Technology Dependency Graph (TDG). Firstly, an adversarial multitask learning and distantly-supervised learning were applied to extract the technology entity and dependency relation with limited labeled sample. Then, a TDG was constructed with the entities as vertices and the dependency relations as edges. A TDG contains rich and valuable semantic information which represents the support, contribution or relying on relationship between technologies. At the same time, the social network properties of TDG allow researchers to analyze and mine hot topics, key technologies, and technology architecture by using network theories, methods and tools. In the case study, the TDG of DSSC (dye-sensitized solar cell) was constructed. Furthermore, the technology dependency architecture for DSSC was constructed according to a spanning tree out of the TDG, which provides a global perspective for the research of DSSC.