The paper presents a text mining approach to identifying technological trajectories. The main problem addressed is the selection of documents related to a particular technology. These documents are needed to identify a trajectory of the technology. Two different methods were compared (based on word2vec and lexical-morphological and syntactic search). The aim of developed approach is to retrieve more information about a given technology and about technologies that could affect its development. We present the results of experiments on a dataset containing over 4.4 million of documents as a part of USPTO patent database. Self-driving car technology was chosen as an example. The result of the research shows that the developed methods are useful for automated information retrieval as the first stage of the analysis and identification of technological trajectories.
PDF at SpringerLink: https://link.springer.com/content/pdf/10.1007%2F978-3-030-30763-9_12.pdf
Volkov S. S., Devyatkin D. A., Sochenkov I. V., Tikhomirov I. A., Toganova N. V. Towards Automated Identification of Technological Trajectories // In: Kuznetsov S., Panov A. (eds) Artificial Intelligence. RCAI 2019. Communications in Computer and Information Science, vol. 1093 – Springer, Cham, 2019. pp 143-153.