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Title: GitHub - npubird/KnowledgeGraphCourse: 东南大学《知识图谱》研究生课程

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Description: 东南大学《知识图谱》研究生课程. Contribute to npubird/KnowledgeGraphCourse development by creating an account on GitHub.

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2024-KG+LLM论文分享.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-KG%2BLLM%E8%AE%BA%E6%96%87%E5%88%86%E4%BA%AB.pdf
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2024-pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
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2024-pub-1知识图谱-理论-技术-实践和挑战A.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98A.pdf
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2024-pub-1知识图谱-理论-技术-实践和挑战C.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98C.pdf
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2024-pub-2知识表示.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-2%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA.pdf
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2024-pub-4知识抽取问题与方法.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-4%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96%E9%97%AE%E9%A2%98%E4%B8%8E%E6%96%B9%E6%B3%95.pdf
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2025-pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
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2025-pub-4 知识抽取问题与方法.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-4%20%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96%E9%97%AE%E9%A2%98%E4%B8%8E%E6%96%B9%E6%B3%95.pdf
2025-pub-5-1实体识别研究前沿进展2025.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-5-1%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB%E7%A0%94%E7%A9%B6%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%952025.pdf
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2025-pub-5知识抽取-实体识别.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB.pdf
2025-pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
2025-pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
2025-pub-7-1 知识抽取-从经典到大模型的范式(20231201-华为).pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-7-1%20%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BB%8E%E7%BB%8F%E5%85%B8%E5%88%B0%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%8C%83%E5%BC%8F(20231201-%E5%8D%8E%E4%B8%BA).pdf
2025-pub-7-1 知识抽取-从经典到大模型的范式(20231201-华为).pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-7-1%20%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BB%8E%E7%BB%8F%E5%85%B8%E5%88%B0%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%8C%83%E5%BC%8F(20231201-%E5%8D%8E%E4%B8%BA).pdf
2025-pub-9 知识图谱与大模型前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-9%20%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1%E4%B8%8E%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
2025-pub-9 知识图谱与大模型前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-9%20%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1%E4%B8%8E%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
README.mdhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/README.md
README.mdhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/README.md
pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
pub-11ChatGPT在质量评测和Prompt工程方面的应用.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11ChatGPT%E5%9C%A8%E8%B4%A8%E9%87%8F%E8%AF%84%E6%B5%8B%E5%92%8CPrompt%E5%B7%A5%E7%A8%8B%E6%96%B9%E9%9D%A2%E7%9A%84%E5%BA%94%E7%94%A8.pdf
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pub-11Prompt分享.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11Prompt%E5%88%86%E4%BA%AB.pdf
pub-11Prompt分享.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11Prompt%E5%88%86%E4%BA%AB.pdf
pub-12ChatGPT在信息抽取和多模态方面的应用.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-12ChatGPT%E5%9C%A8%E4%BF%A1%E6%81%AF%E6%8A%BD%E5%8F%96%E5%92%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E6%96%B9%E9%9D%A2%E7%9A%84%E5%BA%94%E7%94%A8.pdf
pub-12ChatGPT在信息抽取和多模态方面的应用.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-12ChatGPT%E5%9C%A8%E4%BF%A1%E6%81%AF%E6%8A%BD%E5%8F%96%E5%92%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E6%96%B9%E9%9D%A2%E7%9A%84%E5%BA%94%E7%94%A8.pdf
pub-1知识图谱-理论-技术-实践和挑战A.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98A.pdf
pub-1知识图谱-理论-技术-实践和挑战A.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98A.pdf
pub-1知识图谱-理论-技术-实践和挑战B.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98B.pdf
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pub-2知识表示.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-2%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA.pdf
pub-2知识表示.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-2%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA.pdf
pub-3知识建模.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-3%E7%9F%A5%E8%AF%86%E5%BB%BA%E6%A8%A1.pdf
pub-3知识建模.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-3%E7%9F%A5%E8%AF%86%E5%BB%BA%E6%A8%A1.pdf
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pub-4知识抽取-问题与方法.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-4%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E9%97%AE%E9%A2%98%E4%B8%8E%E6%96%B9%E6%B3%95.pdf
pub-5实体识别研究前沿进展2022.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-5%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB%E7%A0%94%E7%A9%B6%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%952022.pdf
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pub-5知识抽取-实体识别.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB.pdf
pub-5知识抽取-实体识别.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB.pdf
pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
pub-7知识抽取-事件抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-7%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96.pdf
pub-7知识抽取-事件抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-7%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96.pdf
pub-8知识融合.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88.pdf
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pub-8知识融合前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
pub-8知识融合前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
pub-9知识表示学习A.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-9%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA%E5%AD%A6%E4%B9%A0A.pdf
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pub-9知识表示学习B.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-9%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA%E5%AD%A6%E4%B9%A0B.pdf
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READMEhttps://github.com/npubird/KnowledgeGraphCourse
https://github.com/npubird/KnowledgeGraphCourse#a-systematic-course-about-knowledge-graph-for-graduate-students-interested-researchers-and-engineers
https://github.com/npubird/KnowledgeGraphCourse#课程内容
https://github.com/npubird/KnowledgeGraphCourse#第0讲-课程介绍2025-02-21
2025-pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
https://github.com/npubird/KnowledgeGraphCourse#第1讲-知识图谱概论-2025-02-2102-2803-07
2025-partAhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-1%20%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98A.pdf
2025-partBhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-1%20%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98B.pdf
https://github.com/npubird/KnowledgeGraphCourse#第2讲-知识表示-2025-03-07
2025-pub-2知识表示.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-2%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA.pdf
https://github.com/npubird/KnowledgeGraphCourse#第3讲-知识建模-2025-03-14
2025-pub-3知识建模.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-3%E7%9F%A5%E8%AF%86%E5%BB%BA%E6%A8%A1.pdf
https://github.com/npubird/KnowledgeGraphCourse#第4讲-知识抽取基础问题和方法2025-03-14
2025-pub-4知识抽取问题与方法.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-4%20%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96%E9%97%AE%E9%A2%98%E4%B8%8E%E6%96%B9%E6%B3%95.pdf
https://github.com/npubird/KnowledgeGraphCourse#第5讲-知识抽取实体识别2025-03-21
2025-pub-5知识抽取-实体识别.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB.pdf
2025-pub-5.1实体识别研究前沿进展2023.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-5-1%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB%E7%A0%94%E7%A9%B6%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%952025.pdf
https://github.com/npubird/KnowledgeGraphCourse#第6讲-知识抽取关系抽取2025-03-28
2025-pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
https://github.com/npubird/KnowledgeGraphCourse#第7讲-知识抽取事件抽取2025-04-11
2025-pub-7知识抽取-事件抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-7%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96.pdf
2025-pub-7-1 知识抽取-事件抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-7-1%20%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BB%8E%E7%BB%8F%E5%85%B8%E5%88%B0%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%8C%83%E5%BC%8F(20231201-%E5%8D%8E%E4%B8%BA).pdf
https://github.com/npubird/KnowledgeGraphCourse#第8讲-知识融合2025-04-18
2025-pub-8 知识融合.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88.pdf
https://github.com/npubird/KnowledgeGraphCourse#第9讲-知识图谱与大语言模型前沿进展2025-04-25
2025-pub-9 知识图谱与大模型前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2025-pub-9%20%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1%E4%B8%8E%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
https://github.com/npubird/KnowledgeGraphCourse#第0讲-课程介绍-2024-02-22
2024-pub-0课程介绍.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-0%E8%AF%BE%E7%A8%8B%E4%BB%8B%E7%BB%8D.pdf
https://github.com/npubird/KnowledgeGraphCourse#第1讲-知识图谱概论-2024-02-2903-0803-14
2024-partAhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98A.pdf
2024-partBhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98B.pdf
2024-partChttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-1%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-%E7%90%86%E8%AE%BA-%E6%8A%80%E6%9C%AF-%E5%AE%9E%E8%B7%B5%E5%92%8C%E6%8C%91%E6%88%98C.pdf
https://github.com/npubird/KnowledgeGraphCourse#第2讲-知识表示-2024-03-21
2024-pub-2知识表示.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-2%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA.pdf
https://github.com/npubird/KnowledgeGraphCourse#第3讲-知识建模-2024-03-21
2024-pub-3知识建模.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-3%E7%9F%A5%E8%AF%86%E5%BB%BA%E6%A8%A1.pdf
https://github.com/npubird/KnowledgeGraphCourse#第4讲-知识抽取基础问题和方法2024-03-28
2024-pub-4知识抽取问题与方法.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024pub-4%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96%E9%97%AE%E9%A2%98%E4%B8%8E%E6%96%B9%E6%B3%95.pdf
https://github.com/npubird/KnowledgeGraphCourse#第5讲-知识抽取实体识别2024-03-28
2024-pub-5知识抽取-实体识别.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB.pdf
2024-pub-5知识抽取-从经典到大模型的范式.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-5%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BB%8E%E7%BB%8F%E5%85%B8%E5%88%B0%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%8C%83%E5%BC%8F.pdf
2024-pub-5.1实体识别研究前沿进展2023.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-5.1%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB%E7%A0%94%E7%A9%B6%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%952023.pdf
https://github.com/npubird/KnowledgeGraphCourse#第6讲-知识抽取关系抽取2024-04-11
2024-pub-6知识抽取-关系抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-6%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96.pdf
https://github.com/npubird/KnowledgeGraphCourse#第7讲-知识抽取事件抽取2024-04-25
2024-pub-7知识抽取-事件抽取.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-7%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96.pdf
https://github.com/npubird/KnowledgeGraphCourse#第8讲-知识融合2024-04-25
2024-pub-8知识融合.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88.pdf
2024-pub-8知识融合前沿进展.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-8%E7%9F%A5%E8%AF%86%E8%9E%8D%E5%90%88%E5%89%8D%E6%B2%BF%E8%BF%9B%E5%B1%95.pdf
https://github.com/npubird/KnowledgeGraphCourse#第9讲-知识图谱表示学习2024-04-28
2024-pub-9知识表示学习https://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-pub-9%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA%E5%AD%A6%E4%B9%A0.pdf
https://github.com/npubird/KnowledgeGraphCourse#2024-第10讲-kgllm论文分享2024-04-252024-04-28
2024-KG+LLM论文分享https://github.com/npubird/KnowledgeGraphCourse/blob/master/2024-KG+LLM%E8%AE%BA%E6%96%87%E5%88%86%E4%BA%AB.pdf
https://github.com/npubird/KnowledgeGraphCourse#2023-第10讲-chatgpt到kg2023
https://github.com/npubird/KnowledgeGraphCourse#2023-第11讲-chatgpt相关论文分享2023-5-5
pub-11Prompt分享.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11Prompt%E5%88%86%E4%BA%AB.pdf
pub-11ChatGPT在质量评测和Prompt工程方面的应用.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11ChatGPT%E5%9C%A8%E8%B4%A8%E9%87%8F%E8%AF%84%E6%B5%8B%E5%92%8CPrompt%E5%B7%A5%E7%A8%8B%E6%96%B9%E9%9D%A2%E7%9A%84%E5%BA%94%E7%94%A8.pdf
pub-11Prompt分享.pdfhttps://pan.baidu.com/s/1zl_Ie0_0541Kkt0uDL481Q
pub-11ChatGPT在质量评测和Prompt工程方面的应用.pdfhttps://pan.baidu.com/s/1-5aqKkiT1ot3XYQpmlxSyQ
https://github.com/npubird/KnowledgeGraphCourse#第12讲-chatgpt相关论文分享2023-5-12
pub-12ChatGPT在信息抽取和多模态方面的应用.pdfhttps://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-12ChatGPT%E5%9C%A8%E4%BF%A1%E6%81%AF%E6%8A%BD%E5%8F%96%E5%92%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E6%96%B9%E9%9D%A2%E7%9A%84%E5%BA%94%E7%94%A8.pdf
pub-12ChatGPT在信息抽取和多模态方面的应用.pdfhttps://pan.baidu.com/s/136vQ1BUJRQ_HSXZDM7u94g
https://github.com/npubird/KnowledgeGraphCourse#附录a经典文献选读
https://github.com/npubird/KnowledgeGraphCourse#知识图谱构建
Knowledge vault: A web-scale approach to probabilistic knowledge fusionhttps://ai.google/research/pubs/pub45634.pdf
Yago: a core of semantic knowledgehttp://www2007.wwwconference.org/papers/paper391.pdf
YAGO2: A spatially and temporally enhanced knowledge base from Wikipediahttps://people.mpi-inf.mpg.de/~kberberi/publications/2013-ai.pdf
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic networkhttp://web.informatik.uni-mannheim.de/ponzetto/pubs/navigli12b.pdf
Dbpedia: A nucleus for a web of open datahttp://editthis.info/images/swim/d/d8/Dbpedia_-_open_data.pdf
Never-ending learninghttps://dl.acm.org/ft_gateway.cfm?id=3191513&type=pdf
earlier workhttps://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1879/2201
Query-driven on-the-fly knowledge base construction.http://orbilu.uni.lu/bitstream/10993/34035/1/p66-nguyen.pdf
Conceptnet 5.5: An open multilingual graph of general knowledgehttps://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14972/14051
https://github.com/npubird/KnowledgeGraphCourse#知识表示和建模
Knowledge representation: logical, philosophical, and computational foundationshttps://www.aclweb.org/anthology/J01-2006.pdf
Ontology Development 101: A Guide to Creating Your First Ontologyhttp://ftp.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness.pdf
another versionhttp://www.corais.org/sites/default/files/ontology_development_101_aguide_to_creating_your_first_ontology.pdf
https://github.com/npubird/KnowledgeGraphCourse#知识抽取
Web-scale information extraction in knowitall:(preliminary results)http://www2004.org/proceedings/docs/1p100.pdf
Open information extraction from the webhttps://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf
Information extractionhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.442.2007&rep=rep1&type=pdf
Identifying relations for open information extractionhttps://aclanthology.info/pdf/D/D11/D11-1142.pdf
Automatic knowledge extraction from documentshttp://brenocon.com/watson_special_issue/05%20automatic%20knowledge%20extration.pdf
Automatic acquisition of hyponyms from large text corporahttp://www.aclweb.org/anthology/C92-2082
A survey of named entity recognition and classificationhttps://www.jbe-platform.com/content/journals/10.1075/li.30.1.03nad
Neural architectures for named entity recognitionhttps://arxiv.org/pdf/1603.01360.pdf
Bidirectional LSTM-CRF models for sequence tagginghttps://arxiv.org/pdf/1508.01991.pdf
Graph ranking for collective named entity disambiguationhttp://www.aclweb.org/anthology/P14-2013
Named entity recognition through classifier combinationhttp://www.aclweb.org/anthology/W03-0425
Named entity recognition with bidirectional LSTM-CNNshttps://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00104
Learning multilingual named entity recognition from Wikipediahttps://www.sciencedirect.com/science/article/pii/S0004370212000276
Boosting named entity recognition with neural character embeddingshttps://arxiv.org/pdf/1505.05008
Domain adaptation of rule-based annotators for named-entity recognition taskshttp://www.aclweb.org/anthology/D10-1098
A survey of arabic named entity recognition and classificationhttps://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00178
Ensemble learning for named entity recognitionhttps://svn.aksw.org/papers/2014/ISWC_EL4NER/public.pdf
Deep learning with word embeddings improves biomedical named entity recognitionhttps://academic.oup.com/bioinformatics/article/33/14/i37/3953940
Relation extraction and scoring in DeepQAhttp://brenocon.com/watson_special_issue/09%20relation%20extraction%20and%20scoring.pdf
Semantic compositionality through recursive matrix-vector spaceshttps://www.aclweb.org/anthology/D12-1110
Convolution neural network for relation extractionhttps://link.springer.com/content/pdf/10.1007%2F978-3-642-53917-6.pdf
Relation classification via convolutional deep neural networkhttp://ir.ia.ac.cn/bitstream/173211/4797/1/Relation%20Classification%20via%20Convolutional%20Deep%20Neural%20Network.pdf
“Classifying relations by ranking with convolutional neural networks.”https://www.aclweb.org/anthology/P15-1061
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networkshttps://www.aclweb.org/anthology/D15-1203
End-to-end Relation Extraction using LSTMs on Sequences and Tree Structureshttps://www.aclweb.org/anthology/P16-1105
Attention-based bidirectional long short-term memory networks for relation classificationhttp://anthology.aclweb.org/P16-2034
Neural relation extraction with selective attention over instanceshttps://www.aclweb.org/anthology/P16-1200
Bidirectional recurrent convolutional neural network for relation classificationhttps://www.aclweb.org/anthology/P16-1072
Relation classification via multi-level attention cnnshttp://eprints.bimcoordinator.co.uk/14/1/relation-classification.pdf
Attention-based bidirectional long short-term memory networks for relation classificationhttps://www.aclweb.org/anthology/P16-2034
Neural relation extraction with selective attention over instanceshttps://www.aclweb.org/anthology/P16-1200
Neural relation extraction with multi-lingual attentionhttps://www.aclweb.org/anthology/P17-1004
Deep residual learning for weakly-supervised relation extractionhttps://www.aclweb.org/anthology/D17-1191
Distant supervision for relation extraction with sentence-level attention and entity descriptionshttps://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14491/14078
Adversarial training for relation extractionhttps://www.aclweb.org/anthology/D17-1187
Cotype: Joint extraction of typed entities and relations with knowledge baseshttps://www.ijcai.org/proceedings/2018/0620.pdf
Event extraction via dynamic multi-pooling convolutional neural networkshttp://www.aclweb.org/anthology/P15-1017
Event detection and domain adaptation with convolutional neural networkshttp://www.aclweb.org/anthology/P15-2060
An overview of event extraction from texthttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.369.7040&rep=rep1&type=pdf
Improving information extraction by acquiring external evidence with reinforcement learninghttps://arxiv.org/pdf/1603.07954.pdf
Joint event extraction via recurrent neural networkshttp://www.aclweb.org/anthology/N16-1034
https://github.com/npubird/KnowledgeGraphCourse#知识融合
Ontology matching: state of the art and future challengeshttps://hal.inria.fr/hal-00917910/document
Algorithm and tool for automated ontology merging and alignmenthttps://www.aaai.org/Papers/AAAI/2000/AAAI00-069.pdf
COMA: a system for flexible combination of schema matching approacheshttp://www.vldb.org/conf/2002/S17P03.pdf
Learning to map between ontologies on the semantic webhttp://secs.ceas.uc.edu/~mazlack/CS716.f2006/Semantic.Web.Ontology.Papers/Doan.02.pdf
QOM–quick ontology mappinghttp://www.scs.carleton.ca/~armyunis/knowledge-managment/papers/QOM-Quick%20Ontology%20Mapping.pdf
Constructing virtual documents for ontology matchinghttps://www.researchgate.net/profile/Yuzhong_Qu/publication/221022499_Lecture_Notes_in_Computer_Science/links/5483bb9f0cf25dbd59eb0ff0/Lecture-Notes-in-Computer-Science.pdf
RiMOM: A dynamic multistrategy ontology alignment frameworkhttps://ieeexplore.ieee.org/abstract/document/4633358/
An adaptive ontology mapping approach with neural network based constraint satisfactionhttp://gesispanel.gesis.org/preprints/index.php/ps/article/download/209/368
Matching large ontologies: A divide-and-conquer approachhttp://dit.unitn.it/~p2p/RelatedWork/Matching/MatchingLargeOntologies.pdf
A blocking framework for entity resolution in highly heterogeneous information spaceshttp://disi.unitn.it/~themis/publications/erframework-tr12.pdf
Matching large ontologies based on reduction anchorshttps://www.aaai.org/ocs/index.php/IJCAI/IJCAI11/paper/download/3145/3697
An effective rule miner for instance matching in a web of datahttp://xingniu.org/pub/ruleminer_cikm12.pdf
A blocking framework for entity resolution in highly heterogeneous information spaceshttp://disi.unitn.it/~themis/publications/erframework-tr12.pdf
Large scale instance matching via multiple indexes and candidate selectionhttp://disi.unitn.it/~p2p/RelatedWork/Matching/KBS13-Li-et-al-large-instance.pdf
A self-training approach for resolving object coreference on the semantic webhttp://dit.unitn.it/~p2p/RelatedWork/Matching/A%20self-training%20approach_Hu_www11.pdf
A unified probabilistic framework for name disambiguation in digital libraryhttp://keg.cs.tsinghua.edu.cn/jietang/publications/TKDE12-Tang-Name-Disambiguation.pdf
Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loophttp://keg.cs.tsinghua.edu.cn/jietang/publications/kdd18_yutao-AMiner-Name-Disambiguation.pdf
LIMES—a time-efficient approach for large-scale link discovery on the web of datahttps://www.aaai.org/ocs/index.php/IJCAI/IJCAI11/paper/viewFile/3125/3692
Cross-lingual entity alignment via joint attribute-preserving embedding.https://arxiv.org/pdf/1708.05045
Robust disambiguation of named entities in text.https://www.aclweb.org/anthology/D11-1072
https://github.com/npubird/KnowledgeGraphCourse#知识图谱嵌入
Knowledge graph embedding: A survey of approaches and applicationshttp://download.xuebalib.com/3at6CEQL3eBi.pdf
知识表示学习研究进展http://crad.ict.ac.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=3099
Word representations: A simple and general method for semi-supervised learninghttps://aclanthology.info/pdf/P/P10/P10-1040.pdf
Joint learning of words and meaning representations for open-text semantic parsinghttp://proceedings.mlr.press/v22/bordes12/bordes12.pdf
Learning structured embeddings of knowledge baseshttps://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898
Distributed representations of words and phrases and their compositionalityhttps://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
Translating embeddings for modeling multi-relational datahttp://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf
Knowledge graph embedding by translating on hyperplaneshttps://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546
Learning entity and relation embeddings for knowledge graph completionhttps://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523
Knowledge graph embedding via dynamic mapping matrixhttp://www.aclweb.org/anthology/P15-1067
Knowledge graph completion with adaptive sparse transfer matrixhttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/11982/11693
Transition-based knowledge graph embedding with relational mapping propertieshttps://www.aclweb.org/anthology/Y14-1039
Knowledge graph embedding for precise link predictionhttps://arxiv.org/pdf/1512.04792
Knowledge graph embedding by flexible translationhttps://www.aaai.org/ocs/index.php/KR/KR16/paper/viewPDFInterstitial/12887/12520
TransA: An adaptive approach for knowledge graph embeddinghttps://arxiv.org/pdf/1509.05490
Learning to represent knowledge graphs with gaussian embeddinghttp://ir.ia.ac.cn/bitstream/173211/11475/1/sig-alternate.pdf
TransG: A generative model for knowledge graph embeddinghttps://www.aclweb.org/anthology/P16-1219
A latent factor model for highly multi-relational datahttps://papers.nips.cc/paper/4744-a-latent-factor-model-for-highly-multi-relational-data.pdf
A Three-Way Model for Collective Learning on Multi-Relational Datahttp://www.cip.ifi.lmu.de/~nickel/data/slides-icml2011.pdf
Embedding entities and relations for learning and inference in knowledge baseshttps://arxiv.org/pdf/1412.6575
Holographic embeddings of knowledge graphshttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828
Complex embeddings for simple link predictionhttp://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf
Analogical inference for multi-relational embeddingshttps://arxiv.org/pdf/1705.02426
Reasoning with neural tensor networks for knowledge base completionhttps://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf
A semantic matching energy function for learning with multi-relational datahttps://link.springer.com/article/10.1007/s10994-013-5363-6
Reasoning with neural tensor networks for knowledge base completionhttps://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf
Knowledge vault: A web-scale approach to probabilistic knowledge fusionhttps://ai.google/research/pubs/pub45634.pdf
Probabilistic reasoning via deep learning: Neural association modelshttps://arxiv.org/pdf/1603.07704
Convolutional 2d knowledge graph embeddingshttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884
Semantically smooth knowledge graph embeddinghttps://www.aclweb.org/anthology/P15-1009
Representation Learning of Knowledge Graphs with Hierarchical Typeshttp://nlp.csai.tsinghua.edu.cn/~xrb/publications/IJCAI-16_type.pdf
Modeling relation paths for representation learning of knowledge baseshttps://arxiv.org/pdf/1506.00379
Knowledge vault: A web-scale approach to probabilistic knowledge fusionhttps://ai.google/research/pubs/pub45634.pdf
Reducing the rank in relational factorization models by including observable patternshttp://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf
Reasoning with neural tensor networks for knowledge base completionhttps://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf
Representation learning of knowledge graphs with entity descriptionshttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12216/12004
SSP: semantic space projection for knowledge graph embedding with text descriptionshttps://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPDFInterstitial/14306/14084
Text-Enhanced Representation Learning for Knowledge Graphhttp://qngw2014.bj.bcebos.com/upload/2016/04/%E7%8E%8B%E5%BF%97%E5%88%9A-Text-enhanced%20Representation%20Learning%20for%20Knowledge%20Graph.pdf
Knowledge graph and text jointly embeddinghttps://www.aclweb.org/anthology/D14-1167
Knowledge base completion using embeddings and ruleshttps://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/10798/10921
Jointly embedding knowledge graphs and logical ruleshttps://www.aclweb.org/anthology/D16-1019
Knowledge graph embedding with iterative guidance from soft ruleshttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011
Improving knowledge graph embedding using simple constraintshttps://arxiv.org/pdf/1805.02408
Factorizing yago: scalable machine learning for linked datahttp://www.dbs.ifi.lmu.de/~tresp/papers/p271.pdf
Encoding temporal information for time-aware link predictionhttps://www.aclweb.org/anthology/D16-1260
GAKE: graph aware knowledge embeddinghttps://aclanthology.info/pdf/C/C16/C16-1062.pdf
https://github.com/npubird/KnowledgeGraphCourse#知识推理知识挖掘
A Three-Way Model for Collective Learning on Multi-Relational Datahttp://www.cip.ifi.lmu.de/~nickel/data/slides-icml2011.pdf
Reasoning with neural tensor networks for knowledge base completionhttps://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf
Relational retrieval using a combination of path-constrained random walkshttps://link.springer.com/content/pdf/10.1007/s10994-010-5205-8.pdf
Modeling relation paths for representation learning of knowledge baseshttp://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP082.pdf
Incorporating vector space similarity in random walk inference over knowledge baseshttp://www.aclweb.org/anthology/D14-1044
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoninghttp://www.aclweb.org/anthology/D17-1060
Reasoning With Neural Tensor Networks for Knowledge Base Completion[C]https://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pdf
ProjE: Embedding Projection for Knowledge Graph Completion[J]https://arxiv.org/pdf/1611.05425.pdf
Open-World Knowledge Graph Completion[J]https://arxiv.org/pdf/1711.03438.pdf
Modeling Relational Data with Graph Convolutional Networks[J]https://arxiv.org/pdf/1703.06103.pdf
Iterative Entity Alignment via Joint Knowledge Embeddings[C]http://nlp.csai.tsinghua.edu.cn/~lzy/publications/ijcai2017_entity.pdf
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks[J]https://arxiv.org/pdf/1607.01426.pdf
Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion[J]https://128.84.21.199/pdf/1611.04642v2.pdf
Hybrid computing using a neural network with dynamic external memory[J]https://www.nature.com/articles/nature20101.pdf
Differentiable Learning of Logical Rules for Knowledge Base Reasoning[J]https://arxiv.org/pdf/1702.08367.pdf
https://github.com/npubird/KnowledgeGraphCourse#实体链接
Entity linking leveraging: automatically generated annotation[C]https://www.aclweb.org/anthology/C10-1145
Supervised learning for linking named entities to knowledge base entries[C]https://tac.nist.gov//publications/2011/participant.papers/dmir_inescid.proceedings.pdf
Capturing semantic similarity for entity linking with convolutional neural networks[C]https://arxiv.org/pdf/1604.00734.pdf
Modeling mention, context and entity with neural networks for entity disambiguationhttps://www.ijcai.org/Proceedings/15/Papers/192.pdf
Collective entity linking in web text: a graph-based method[C]http://www.nlpr.ia.ac.cn/2011papers/gjhy/gh133.pdf
Entity linking: Finding extracted entities in a knowledge basehttp://www.cs.jhu.edu/~delip/entity_linking.pdf
Robust entity linking via random walks[C]https://dl.acm.org/citation.cfm?id=2661887
https://github.com/npubird/KnowledgeGraphCourse#知识存储知识查询
Building an efficient RDF store over a relational databasehttps://www.researchgate.net/profile/Patrick_Dantressangle/publication/262162010_Building_an_efficient_RDF_store_over_a_relational_database/links/54f718680cf210398e9184bc/Building-an-efficient-RDF-store-over-a-relational-database.pdf
Scalable SPARQL querying of large RDF graphshttp://www.cs.umd.edu/~abadi/papers/sw-graph-scale.pdf
gStore: a graph-based SPARQL query enginehttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.386.7427&rep=rep1&type=pdf
Efficient RDF storage and retrieval in Jena2http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.221.3451&rep=rep1&type=pdf#page=137
gStore: answering SPARQL queries via subgraph matchinghttp://www.vldb.org/pvldb/vol4/p482-zou.pdf
G-store: a scalable data store for transactional multi key access in the cloudhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.209.1087&rep=rep1&type=pdf
gStore: a graph-based SPARQL query enginehttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.386.7427&rep=rep1&type=pdf
RStar: an RDF storage and query system for enterprise resource managementhttps://www.researchgate.net/profile/Zhong_Su/publication/221614612_RStar_An_RDF_storage_and_query_system_for_enterprise_resource_management/links/02e7e5181cc6d57b09000000/RStar-An-RDF-storage-and-query-system-for-enterprise-resource-management.pdf
A distributed graph engine for web scale RDF datahttps://www.graphengine.io/downloads/papers/Trinity.RDF.pdf
Relational processing of RDF queries: a surveyhttps://sigmodrecord.org/publications/sigmodRecord/0912/p23.survey.sakr.pdf
SPARQL query processing with conventional relational database systemshttps://eprints.soton.ac.uk/261126/1/harris-ssws05.pdf
A comparison of current graph database modelshttps://www.researchgate.net/profile/Renzo_Angles/publication/261076480_A_Comparison_of_Current_Graph_Database_Models/links/54f05b180cf25f74d72609c3.pdf
Graph database applications and concepts with Neo4jhttps://pdfs.semanticscholar.org/322a/6e1f464330751dea2eb6beecac24466322ad.pdf
HyperGraphDB: a generalized graph databasehttps://www.researchgate.net/profile/Borislav_Iordanov/publication/225204980_HyperGraphDB_A_Generalized_Graph_Database/links/0fcfd509bfc6de5b9a000000/HyperGraphDB-A-Generalized-Graph-Database.pdf
Scalable rdf store based on hbase and mapreducehttps://ieeexplore.ieee.org/abstract/document/5578937/
Scalable SPARQL querying of large RDF graphshttp://www.cs.umd.edu/~abadi/papers/sw-graph-scale.pdf
Hexastore: sextuple indexing for semantic web data managementhttp://people.csail.mit.edu/tdanford/6830papers/weiss-hexastore.pdf
The RDF-3X engine for scalable management of RDF datahttps://pure.mpg.de/rest/items/item_1324253/component/file_1324252/content
https://github.com/npubird/KnowledgeGraphCourse#人机交互
Introduction to “this is watson”https://ieeexplore.ieee.org/abstract/document/6177724/
Question analysis: How Watson reads a cluehttp://www.patwardhans.net/papers/LallyEtAl12.pdf
Commonsense Knowledge Aware Conversation Generation with Graph Attentionhttps://www.ijcai.org/proceedings/2018/0643.pdf
Building a large-scale multimodal knowledge base system for answering visual querieshttps://pdfs.semanticscholar.org/9563/d6fafb6ba09c082a57e8d9b31494029a45ac.pdf
Joint language and translation modeling with recurrent neural networkshttp://www.aclweb.org/anthology/D13-1106
Neural machine translation by jointly learning to align and translatehttps://arxiv.org/abs/1409.0473
Learning phrase representations using RNN encoder-decoder for statistical machine translationhttp://anthology.aclweb.org/D/D14/D14-1179.pdf
Empirical evaluation of gated recurrent neural networks on sequence modelinghttps://arxiv.org/abs/1412.3555
Generating sequences with recurrent neural networkshttps://arxiv.org/abs/1308.0850
https://github.com/npubird/KnowledgeGraphCourse#附录b最新进展论文选读近1年内
Finding Descriptive Support Passages for Knowledge Graph Relationshipshttp://sumitbhatia.net/papers/iswc18.pdf
Representativeness of Knowledge Bases with the Generalized Benford’s Lawhttps://a3nm.net/work/seminar/slides/20181115-soulet.pdf
Towards Empty Answers in SPARQL: Approximating Querying with RDF Embeddinghttps://link.springer.com/chapter/10.1007/978-3-030-00671-6_30
Canonicalisation of monotone SPARQL querieshttps://link.springer.com/chapter/10.1007/978-3-030-00671-6_35
Ontology Driven Extraction of Research Processeshttps://pages.cs.aueb.gr/ipl/nlp/pubs/iswc2018.pdf
Using link features for entity clustering in knowledge graphshttps://dbs.uni-leipzig.de/file/eswc_0.pdf
Modeling relational data with graph convolutional networkshttps://arxiv.org/pdf/1703.06103
The Design and Implementation of XiaoIce, an Empathetic Social Chatbothttps://arxiv.org/pdf/1812.08989
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embeddinghttp://www.aclweb.org/anthology/D18-1225
EARL: Joint entity and relation linking for question answering over knowledge graphshttps://arxiv.org/pdf/1801.03825
Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignmenthttp://yellowstone.cs.ucla.edu/~muhao/slides/kdcoe.pdf
Impact analysis of data placement strategies on query efforts in distributed rdf storeshttp://mail.websemanticsjournal.org/preprints/index.php/ps/article/view/516/533
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationhttps://arxiv.org/pdf/1810.10147
Sequence-to-Sequence Data Augmentation for Dialogue Language Understandinghttp://www.aclweb.org/anthology/C18-1105
Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systemshttp://www.aclweb.org/anthology/C18-1103
Context-Sensitive Generation of Open-Domain Conversational Responseshttp://www.aclweb.org/anthology/C18-1206
Sentiment Adaptive End-to-End Dialog Systemshttp://www.aclweb.org/anthology/P18-1140
Personalizing Dialogue Agents: I have a dog, do you have pets too?http://www.aclweb.org/anthology/P18-1205
Task-oriented dialogue system for automatic diagnosishttp://www.aclweb.org/anthology/P18-2033
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogueshttps://github.com/npubird/KnowledgeGraphCourse/blob/master
Neural Transfer Learning for Natural Language Processinghttp://ruder.io/thesis/neural_transfer_learning_for_nlp.pdf
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences.https://arxiv.org/pdf/1902.06236
codehttps://github.com/TaoMiner/joint-kg-recommender
https://arxiv.org/pdf/1904.07391https://arxiv.org/pdf/1904.07391
Building language models for text with named entitieshttps://arxiv.org/pdf/1805.04836.pdf
A multi-lingual multi-task architecture for low-resource sequence labelinghttp://www.aclweb.org/anthology/P18-1074
Double embeddings and cnn-based sequence labeling for aspect extractionhttps://arxiv.org/pdf/1805.04601.pdf
Hybrid semi-markov crf for neural sequence labelinghttps://arxiv.org/pdf/1805.03838.pdf
Ncrf++: An open-source neural sequence labeling toolkithttps://arxiv.org/pdf/1806.05626.pdf
A neural layered model for nested named entity recognitionhttp://www.aclweb.org/anthology/N18-1131
Label-aware double transfer learning for cross-specialty medical named entity recognitionhttps://arxiv.org/pdf/1804.09021.pdf
Multimodal named entity recognition for short social ../media postshttps://arxiv.org/pdf/1802.07862.pdf
Nested named entity recognition revisitedhttp://www.aclweb.org/anthology/N18-1079
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanismhttp://www.aclweb.org/anthology/D18-1017
Neural cross-lingual named entity recognition with minimal resourceshttps://arxiv.org/pdf/1808.09861.pdf
Neural adaptation layers for cross-domain named entity recognitionhttps://arxiv.org/pdf/1810.06368.pdf
Learning Named Entity Tagger using Domain-Specific Dictionaryhttps://arxiv.org/pdf/1809.03599.pdf
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Setshttp://www.aclweb.org/anthology/D18-1306
Deep Exhaustive Model for Nested Named Entity Recognitionhttp://www.aclweb.org/anthology/D18-1309
On the Strength of Character Language Models for Multilingual Named Entity Recognitionhttps://arxiv.org/pdf/1809.05157.pdf
An empirical study on fine-grained named entity recognitionhttp://www.aclweb.org/anthology/C18-1060
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classificationhttp://www.aclweb.org/anthology/C18-1196
Exploiting Structure in Representation of Named Entities using Active Learninghttp://www.aclweb.org/anthology/C18-1058
A survey on recent advances in named entity recognition from deep learning modelshttp://www.aclweb.org/anthology/C18-1182
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tagshttps://arxiv.org/pdf/1807.06683.pdf
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Modelshttp://www.aclweb.org/anthology/C18-1185
Robust lexical features for improved neural network named-entity recognitionhttps://arxiv.org/pdf/1806.03489.pdf
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structureshttp://www.aclweb.org/anthology/P18-1045
Nugget Proposal Networks for Chinese Event Detectionhttps://arxiv.org/pdf/1805.00249.pdf
Zero-shot transfer learning for event extractionhttps://arxiv.org/pdf/1707.01066.pdf
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detectionhttp://www.aclweb.org/anthology/P18-1048
Document embedding enhanced event detection with hierarchical and supervised attentionhttp://www.aclweb.org/anthology/P18-2066
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Datahttp://www.aclweb.org/anthology/P18-4009
Semi-Supervised Event Extraction with Paraphrase Clustershttps://arxiv.org/pdf/1808.08622.pdf
Event Detection with Neural Networks: A Rigorous Empirical Evaluationhttps://arxiv.org/pdf/1808.08504.pdf
Exploiting Contextual Information via Dynamic Memory Network for Event Detectionhttps://arxiv.org/pdf/1810.03449.pdf
Jointly multiple events extraction via attention-based graph information aggregationhttps://arxiv.org/pdf/1809.09078.pdf
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanismshttp://www.aclweb.org/anthology/D18-1158
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matchinghttp://www.aclweb.org/anthology/D18-1517
Open-Domain Event Detection using Distant Supervisionhttp://www.aclweb.org/anthology/C18-1075
Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Efforthttp://www.aclweb.org/anthology/C18-1007
Automatically Extracting Qualia Relations for the Rich Event Ontologyhttp://www.aclweb.org/anthology/C18-1224
Graph-Based Decoding for Event Sequencing and Coreference Resolutionhttps://arxiv.org/pdf/1806.05099.pdf
Global relation embedding for relation extractionhttps://www.aclweb.org/anthology/N18-1075
Large scaled relation extraction with reinforcement learninghttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16257/16125
Neural relation extraction via inner-sentence noise reduction and transfer learninghttps://aclweb.org/anthology/D18-1243
Joint Extraction of Entities and Relations Based on a Novel Graph Schemehttp://ir.hit.edu.cn/~car/papers/ijcai18slwang.pdf
Reinforcement learning for relation classification from noisy datahttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17151/16140
SEE: Syntax-aware entity embedding for neural relation extractionhttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16362/16142
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Informationhttps://www.aclweb.org/anthology/D18-1157
Jointly Extracting Multiple Triplets with Multilayer Translation Constraintshttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17151/16140
A Hierarchical Framework for Relation Extraction with Reinforcement Learninghttps://arxiv.org/pdf/1811.03925.pdf
A survey on NoSQL storeshttps://dl.acm.org/citation.cfm?id=3158661
RDF data storage and query processing schemes: A surveyhttps://exascale.info/assets/pdf/wylot2018survey.pdf
Redesign of the gStore systemhttps://link.springer.com/article/10.1007/s11704-018-7212-z
A Scalable Sparse Matrix-Based Join for SPARQL Query Processinghttps://link.springer.com/chapter/10.1007/978-3-030-18590-9_77
TriAL: A navigational algebra for RDF triplestoreshttp://www.research.ed.ac.uk/portal/files/44424184/tripalg_2.pdf
Managing big RDF data in clouds: Challenges, opportunities, and solutionshttps://www.researchgate.net/profile/Ibrar_Yaqoob/publication/323377454_Managing_Big_RDF_Data_in_Clouds_Challenges_Opportunities_and_Solutions/links/5af126810f7e9ba366452ec6/Managing-Big-RDF-Data-in-Clouds-Challenges-Opportunities-and-Solutions.pdf
Multi-hop knowledge graph reasoning with reward shaping.https://arxiv.org/pdf/1808.10568.pdf
Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph.http://journal.pmf.ni.ac.rs/filomat/index.php/filomat/article/download/6806/2760
Variational reasoning for question answering with knowledge graph.https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16983/16176
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs.https://arxiv.org/pdf/1705.05742
Embedding logical queries on knowledge graphs.https://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf
Neural cross-lingual entity linking.https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPDFInterstitial/16501/16101
Bilinear joint learning of word and entity embeddings for Entity Linking.https://www.sciencedirect.com/science/article/pii/S0925231217318234
DeepType: multilingual entity linking by neural type system evolution.https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPDFInterstitial/17148/16094
Neural cross-lingual coreference resolution and its application to entity linking.https://arxiv.org/pdf/1806.10201
Idel: In-database entity linking with neural embeddings.https://arxiv.org/pdf/1803.04884
Neural collective entity linking.https://arxiv.org/pdf/1811.08603
Cross-lingual knowledge graph alignment via graph convolutional networks.https://www.aclweb.org/anthology/D18-1032
MIDAS: Finding the right web sources to fill knowledge gapshttps://people.cs.umass.edu/~xlwang/midas-paper.pdf
Query-driven on-the-fly knowledge base constructionhttp://orbilu.uni.lu/bitstream/10993/34035/1/p66-nguyen.pdf
Efficient knowledge graph accuracy evaluationhttps://arxiv.org/pdf/1907.09657
A Brief History of Knowledge Graph's Main Ideas: A tutorialhttp://knowledgegraph.today/paper.html
Industry-scale knowledge graphs: Lessons and challengeshttps://dl.acm.org/doi/pdf/10.1145/3331166
https://github.com/npubird/KnowledgeGraphCourse#附录b其它资源
Top-level Conference Publications on Knowledge Graph (2018-2020)https://github.com/wds-seu/Knowledge-Graph-Publications
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