Graph Representation Learning Towards Patents Network Analysis
Mohammad Heydari Babak Teimourpour
Patent analysis has recently been recognized as a powerful technique for
large companies worldwide to lend them insight into the age of competition
among various industries. This technique is considered a shortcut for
developing countries since it can significantly accelerate their technology
development. Therefore, as an inevitable process, patent analysis can be
utilized to monitor rival companies and diverse industries. This research
employed a graph representation learning approach to create, analyze, and find
similarities in the patent data registered in the Iranian Official Gazette. The
patent records were scrapped and wrangled through the Iranian Official Gazette
portal. Afterward, the key entities were extracted from the scrapped patents
dataset to create the Iranian patents graph from scratch based on novel natural
language processing and entity resolution techniques. Finally, thanks to the
utilization of novel graph algorithms and text mining methods, we identified
new areas of industry and research from Iranian patent data, which can be used
extensively to prevent duplicate patents, familiarity with similar and
connected inventions, Awareness of legal entities supporting patents and
knowledge of researchers and linked stakeholders in a particular research
field.