| Title | An Improved Bi-LSTM Model for Entity Extraction of Intellectual Property Using Complex Graph |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Youning, Liu, Qi, Wang, Yang |
| Conference Name | 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) |
| Date Published | dec |
| Keywords | composability, Deep Learning, Dictionaries, entity recognition, feature extraction, intellectual property, ip protection, knowledge graph, LSTM, Medical services, policy-based governance, pubcrawl, resilience, Resiliency, smart cities, social networking (online) |
| Abstract | The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition. |
| DOI | 10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00287 |
| Citation Key | wang_improved_2021 |
An Improved Bi-LSTM Model for Entity Extraction of Intellectual Property Using Complex Graph