Year: 2024
Type: conference-paper
Abstract: As competition in intellectual property intensifies, countries around the world are actively engaging in patent activities.The number of patent activities has become an important indicator of international innovation and competitiveness.Patent events refer to the events related to patents, these events can include patent applications, patent grants, patent transfers, patent litigation, and more.The more frequent the patent events, the higher the country's innovation and competitiveness.Accurate and efficient extraction of patent events will help countries understand patent trends and plan their patent activities.Consequently, this has become a popular research topic.The main challenges in patent event extraction include the difficulty in handling unstructured data and the interference of noise with extraction.Additionally, existing event extraction methods have the following limitations:1.Absence of methods that utilize external knowledge related to the data to assist in event extraction, and 2. Lack of awareness in argument, leading to the omission of hidden relationships and features.To solve the above problems, we propose an external knowledge network based patent event extraction method.1.To perform event extraction in the patent domain, we constructed a patent event dataset.2. To enhance argument prompts and connections, we built an external knowledge network incorporating patent data features.3. To represent features, we used multiple attention mechanisms to enhance feature computation.Our method outperforms baseline methods in evaluation metrics, making it more suitable for the automatic extraction of patent events.
Authors: Fuming Ye, Weidong Liu, Yu Zhang, Jiamin Zhang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G1087852652, G3842510892, G7169659999
OpenAlex ID: W4406255342