Oracle Bone Inscriptions information processing based on multi-modal knowledge graph. (June 2021)

• Record Type: Journal Article
• Authors: Xiong, Jing; Liu, Guoying; Liu, Yongge; Liu, Mengting
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Oracle Bone Inscriptions -- Knowledge graph -- Multi-modal data -- Detection and recognition -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107173
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: To solve the problems of the great learning difficulty, the long learning period, wide range of knowledge points but weak knowledge connection, and low sharing of Oracle Bone Studies (OBS), a solution of constructing a multi-modal knowledge graph is proposed. Because OBS research involves various kinds of modal data, and these modalities need to be combined together to solve some problems. The OBS multi-modal knowledge graph can provide a unified semantic space for multi-source heterogeneous data. Through multi-modal fusion and information complementation, the defects of a single modality in information processing can be resolved. This multi-modal knowledge graph organizes and manages the basic data better to serve Oracle Bone Inscriptions (OBI) information processing research. Taking OBI detection and recognition as examples, we studied the applications of OBS multi-modal knowledge graph. The experimental results demonstrate that the proposed method reaches 81.3% accuracy in detection and 80.43% accuracy in recognition, and it has 3.7% in detection and 14.8% in recognition improved to the conventional methods. Graphical abstract: Highlights: A multi-modal knowledge graph is constructed based on multi-source OBS data. OBI detection accuracy is improved by 3.7% based on OBS multi-modal knowledge graph. OBI recognition accuracy is improved by 14.8% using OBS multi-modal knowledge graph.