Presentation
OpenCHM: A MKG-based Education System for Chinese Herbal Medicine
DescriptionChinese herbal medicine (CHM), as a part of traditional Chinese
medicine, is of great importance due to its remarkble performance
on disease prevention, treatment, and daily healthcare. There exists
a wide variety of CHM. In particular, some have similar shapes.
Consequently, we face dilemmas for CHM identificaon. In traditional
education, educators manually displaying pictures of CHM
aim to insist students know the classes of CHM. This way is apparently
time-consuming and labor-intensive. Knowledge graphs
(KGs) are powerful and modern tools to boost model performance
on various applications such as objective detection, natural language
question-answering, and recommendation systems. However,
existing unimodal KGs impede their potential for powerful performance
improvement in downstream tasks. Nevertheless, knowledge
acquisition is multimodal, including text, image, video and audio
modalities. Thus, multimodal knowledge can accumulate from different
views of textual and visual information. Researchers focus
on multimodal knowledge graphs (MKGs). Currently, KGs used for CHM are predominantly unimodal. Due to lacking the professional
knowledge, students find it difficult to gain a comprehensive
perception. Therefore, we propose the OpenCHM, a MKG-based
education system for CHM, which can support image-text retrieval.
Meanwhile, we construct a MKG which integrates texts and images.
Our MKG can provide a wider view to explore further information
of CHM. The service system is suitable for educational demonstrations
as well as real-world applications. Finally, we design several
MKG-based downstream tasks such as knowledge visualization,
multimodal knowledge retrieval and a question-answering platform
for promoting the development of CHM.
medicine, is of great importance due to its remarkble performance
on disease prevention, treatment, and daily healthcare. There exists
a wide variety of CHM. In particular, some have similar shapes.
Consequently, we face dilemmas for CHM identificaon. In traditional
education, educators manually displaying pictures of CHM
aim to insist students know the classes of CHM. This way is apparently
time-consuming and labor-intensive. Knowledge graphs
(KGs) are powerful and modern tools to boost model performance
on various applications such as objective detection, natural language
question-answering, and recommendation systems. However,
existing unimodal KGs impede their potential for powerful performance
improvement in downstream tasks. Nevertheless, knowledge
acquisition is multimodal, including text, image, video and audio
modalities. Thus, multimodal knowledge can accumulate from different
views of textual and visual information. Researchers focus
on multimodal knowledge graphs (MKGs). Currently, KGs used for CHM are predominantly unimodal. Due to lacking the professional
knowledge, students find it difficult to gain a comprehensive
perception. Therefore, we propose the OpenCHM, a MKG-based
education system for CHM, which can support image-text retrieval.
Meanwhile, we construct a MKG which integrates texts and images.
Our MKG can provide a wider view to explore further information
of CHM. The service system is suitable for educational demonstrations
as well as real-world applications. Finally, we design several
MKG-based downstream tasks such as knowledge visualization,
multimodal knowledge retrieval and a question-answering platform
for promoting the development of CHM.
Event Type
Educator's Forum
TimeTuesday, 16 December 202511:15am - 11:30am HKT
LocationMeeting Room S228, Level 2
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