jiuxianghedonglu/MMC-TOP40-Solution
瑞金医院MMC人工智能辅助构建知识图谱大赛TOP40解决方案
This solution helps medical researchers and healthcare professionals automatically identify relationships between different medical entities mentioned within clinical text or research papers. By taking unstructured text documents as input, it outputs a structured understanding of which medical terms are linked and how, which is crucial for building comprehensive medical knowledge graphs. Medical informaticians and clinical data analysts would find this useful for transforming raw text into actionable insights.
No commits in the last 6 months.
Use this if you need to extract and categorize relationships between medical concepts from a large corpus of Chinese clinical or research documents.
Not ideal if your primary goal is named entity recognition without needing to establish relationships between those entities.
Stars
19
Forks
7
Language
Python
License
—
Category
Last pushed
Jan 17, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/jiuxianghedonglu/MMC-TOP40-Solution"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
hyperquest-hq/hyperbase
A foundational library for Semantic Hypergraphs
smilelight/lightKG
基于Pytorch和torchtext的知识图谱深度学习框架。
KRLabsOrg/rulechef
Learn rule-based models from examples using LLM-powered synthesis. Replace expensive LLM calls...
qq547276542/Agriculture_KnowledgeGraph
农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策
totogo/awesome-knowledge-graph
A curated list of Knowledge Graph related learning materials, databases, tools and other resources