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Technology evolution prediction based on multi-relational weighted temporal networks

artificial-intelligence
data-mining-and-machine-learning
data-science
scientific-computing-and-simulation
text-mining

Abstract

Technological innovation is a key force in promoting societal progress and economic development. Emerging technologies do not come out of nowhere, but evolve from existing technologies. However, innovation is not a linear process, but a nonlinear process with the interaction of multiple factors and dynamic changes over time. Based on this, this paper proposes a technology prediction framework, MRWTN (Multi-relationship Weighted Temporal Network), that explores the prediction of unknown technology evolution. The framework constructs a technology network based on the multiple relationships of technology derivation and citation, and combines the text-embedding-ada-002, HeteroGraphSNN, BiLSTM, and Richard Curve model. It deeply explores the technology semantic information, multi-relational structure, temporal differences, and evolving influence, which could scientifically predict the historical missing and future unknown technology evolution trends. Compared with other baselines, the MRWTN framework performed well, and with respect to the technology opportunity prediction task, the MALE metrics and RMSLE metrics showed reductions in empirical analyses of hydrogel, quantum information, brain-computer interface, gene chip, and augmented reality datasets. Technology combinations and recursive features can be effectively captured to explore technological breakthroughs and scientific innovations with combinatorial evolution among technologies.
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