Oracle Bone Inscriptions Multi-Modal Knowledge Graph Construction and Applications

EasyChair Preprint 6266

Abstract

To solve the problems of the great learning difficulty, long learning period, wide range of knowledge points but weak knowledge connection, and low sharing of Oracle Bone Studies (OBS), Artificial Intelligence (AI) techniques are introduced into the traditional study of OBS. We have collected and organized over 120 years of basic Oracle Bone Inscriptions (OBI) data and academic literature to build the OBI big data platform. Based on this platform, we proposed the OBI information processing application pyramid model, which aims to realize the digitization, datafication and intelligence of OBS. And the basis of intelligence is OBS multimodal knowledge graph. 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 modal in OBI information processing can be solved. This multi-modal knowledge graph organizes and manages the basic data better to serve OBI information processing research. Such as OBI detection and recognition, computer aided oracle-bones joint and knowledge question answering assistant. Taking OBI detection and recognition as examples, we studied the applications of the OBS multi-modal knowledge graph. The experimental results showed that the proposed method reached 81.3% accuracy in detection and 80.43% accuracy in recognition, and it had 3.7% in detection and 14.8% in recognition improved to the conventional methods.

Keyphrases: Knowledge Graph, Oracle Bone Inscriptions, deep learning, detection and recognition, multi-modal data

@booklet{EasyChair:6266,
  author    = {Jing Xiong and Guoying Liu and Shuanghao Chen and Yongge Liu and Mengting Liu},
  title     = {Oracle Bone Inscriptions Multi-Modal Knowledge Graph Construction and Applications},
  howpublished = {EasyChair Preprint 6266},
  year      = {EasyChair, 2021}}