MEduKG: Multimodal Educational Knowledge Graph Construction via Large Language Models

MEduKG is a multimodal educational knowledge graph dataset that covers natural sciences (biology, physics, and chemistry) in middle and high school education. It includes multimodal concepts covering text, images, videos, and audio, as well as knowledge points and exercises extracted from curriculum standards and MOOCs. MEduKG comprises 34,630 multimodal concepts and 403,400 triples, making it a vital resource for research in multimodal educational applications.Zip Description`RawData`:  This folder comprises the publicly available raw data, including images derived from course materials, subtitles extracted from MOOC videos, audio files associated with specific concepts, and supplementary files generated through multimodal concept extraction and alignment. `MEduKG ': This folder contains the entire MEduKG, including the triple files of MEduKG as well as the cross-indexing files between multimodal concepts, knowledge points, and images*`Example`: The examples of the Case Study for Multimodal Alignment Section in the paper.`Groundtruth`: The groundtruth for Evaluation on Concept Extraction  Section in the paper.

Document Description

Documents in MEduKG `{subject}Image.json`: This is a cross-indexing file between images and concepts in the multimodal discipline. Each image has a unique ID and is associated with a varying number of concepts.`{subject}Knowledge.json`:  This is a cross-reference file for knowledge points. Each knowledge point has a unique ID, along with URLs for corresponding exercises and IDs of related knowledge points. `{subject}.json`: This is a multimodal concept cross-indexing file involving text, images, videos, audio, and knowledge points. Each multimodal concept has a unique ID.`{subjectTriples}.json`: This file contains all the triples of MEduKG.

Documents in Example

 Each folder contains text, image, video, and audio knowledge of multimodal concepts. To avoid copyright issues, we only provide video URLs and the time intervals where the concepts appear.

Documents in Groundtruth

Each `groundtruth.txt` contains the concepts corresponding to the course with the same name as the folder. The contents were manually annotated by three PhD students and seven master's students, with cross-validation performed to ensure accuracy. The `groundtruth.txt` files are used in the "Evaluation on Concept Extraction" section of the paper to validate the effectiveness of the proposed concept extraction method.

Documents in RawDataIncludes images corresponding to multimodal concepts, as well as intermediate files generated during the construction of the knowledge graph, facilitating the reproducibility of the MEduKG construction process.