The field of education has undergone a significant transformation due to the
rapid advancements in Artificial Intelligence (AI). Among the various AI
technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP)
have emerged as powerful visualization tools for integrating multifaceted
information. In the context of university education, the availability of
numerous specialized courses and complicated learning resources often leads to
inferior learning outcomes for students. In this paper, we propose an automated
framework for knowledge extraction, visual KG construction, and graph fusion,
tailored for the major of Electronic Information. Furthermore, we perform data
analysis to investigate the correlation degree and relationship between
courses, rank hot knowledge concepts, and explore the intersection of courses.
Our objective is to enhance the learning efficiency of students and to explore
new educational paradigms enabled by AI. The proposed framework is expected to
enable students to better understand and appreciate the intricacies of their
field of study by providing them with a comprehensive understanding of the
relationships between the various concepts and courses.

本论文提出了一种针对电子信息专业的自动化知识提取，可视化知识图谱构建和图形融合的框架，旨在通过探索学科交叉的热门知识概念，增强学习效率，提高学生对其专业各个概念和课程之间关系的全面理解，探索由人工智能实现的新教育范式。

为高校课程构建和融合多源教育知识图谱

Multi-source Education Knowledge Graph Construction and Fusion for  College Curricula

Fashion knowledge helps people to dress properly and addresses not only
physiological needs of users, but also the demands of social activities and
conventions. It usually involves three mutually related aspects of: occasion,
person and clothing. However, there are few works focusing on extracting such
knowledge, which will greatly benefit many downstream applications, such as
fashion recommendation. In this paper, we propose a novel method to
automatically harvest fashion knowledge from social media. We unify three tasks
of occasion, person and clothing discovery from multiple modalities of images,
texts and metadata. For person detection and analysis, we use the off-the-shelf
tools due to their flexibility and satisfactory performance. For clothing
recognition and occasion prediction, we unify the two tasks by using a
contextualized fashion concept learning module, which captures the dependencies
and correlations among different fashion concepts. To alleviate the heavy
burden of human annotations, we introduce a weak label modeling module which
can effectively exploit machine-labeled data, a complementary of clean data. In
experiments, we contribute a benchmark dataset and conduct extensive
experiments from both quantitative and qualitative perspectives. The results
demonstrate the effectiveness of our model in fashion concept prediction, and
the usefulness of extracted knowledge with comprehensive analysis.

本篇论文提出了一种从社交媒体中自动提取时尚知识的方法，该方法结合了图像、文本和元数据多种模态中的场合、人物和服装三个方面进行探测，并采用上下文化时尚概念学习模块提供了对于人物检测和服装识别的解决方案，在机器标注数据有限的情况下，通过弱标签建模模块有效地利用了机器标注数据。实验结果证明了我们的模型在时尚概念预测方面的有效性以及提取知识与全面分析的有用性。