Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining

To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation.