External Knowledge Network-Based Patent Event Extraction Model

As competition in intellectual property intensifies, patent activities have become a key indicator to measure international innovation and competitiveness. Patent events refer to events that occur throughout a patent lifecycle, and their frequency directly reflects a country’s innovation and competitive abilities. Therefore, the accurate and efficient extraction of patent events is crucial for countries to track patent development trends and optimize their patent strategies. There is relatively little research on patent event extraction, and its main challenges are: (1) the lack of annotated datasets in the field of patent event extraction restricts the training and evaluation of models. (2) Missing and ambiguous arguments during the extraction process, due to the complexity of the event text. To solve these challenges, we propose an external knowledge network-based patent event extraction method. (1) A patent event dataset is constructed, and common event types and arguments are summarized. (2) An external knowledge network is built based on patent data characteristics, and it helps us better capture event arguments in text. (3) The classification ability of event arguments is enhanced by using adversarial learning and multiple attention mechanisms. Our method is better than the baseline methods in evaluation metrics, making it more suitable for the automatic extraction of patent events.