ModelsKG:A Design and Research on Knowledge Graph of Multimodal Curriculum Based on PaddleOCR and DeepKE

Lei Feng, Zongwu Ke, Na Wu
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引用次数: 1

Abstract

Multimodel deep learning system has attracted more and more attention. The traditional deep learning system mostly focuses on single modal processing and application. However, many applications require various modes to complete a certain task. For example, in the teaching scene, teaching materials are mostly displayed in text mode, video and PPT modes are also used to transfer the content of knowledge points. However, there is often a lack of connection between one mode and another mode, resulting in the fragmentation, complexity and redundancy of knowledge. Based on this consideration, the paper puts forward the design idea and frame of multimodal curriculum knowledge graph based on paddleOCR and DeepKE. Use DeepKE to extract the triple relationship between subject knowledge points and store it in the neo4j graph database, so as to build the knowledge graph of subject knowledge points, then use paddeOCR to identify the text content in the teaching video, generate the video frame description text, use NLP processing technologies such as text similarity to realize the understanding of video segments, and finally link the fine-grained video segments to the text knowledge graph, so as to build the multi-modal curriculum knowledge graph, In order to realize the purpose of intelligent search and intelligent construction of learning link.
ModelsKG:基于PaddleOCR和DeepKE的多模态课程知识图谱设计与研究
多模型深度学习系统越来越受到人们的关注。传统的深度学习系统多侧重于单模态的处理和应用。然而,许多应用程序需要不同的模式来完成某个任务。例如,在教学场景中,教材多以文字方式展示,也采用视频、PPT等方式传递知识点内容。然而,一种模式与另一种模式之间往往缺乏联系,导致知识的碎片化、复杂性和冗余性。基于此,本文提出了基于paddleOCR和DeepKE的多模态课程知识图谱的设计思想和框架。利用DeepKE提取学科知识点之间的三重关系,并将其存储在neo4j图形数据库中,从而构建学科知识点的知识图谱,然后利用paddeOCR识别教学视频中的文本内容,生成视频帧描述文本,利用文本相似度等NLP处理技术实现对视频片段的理解,最后将细粒度视频片段链接到文本知识图谱中。从而构建多模态的课程知识图谱,以实现智能搜索和智能构建学习环节的目的。
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