东南大学《知识图谱》研究生课程 - GitHub

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Type Name Latestcommitmessage Committime README.md UpdateREADME.md Dec31,2020 pub-10知识图谱表示学习.pdf Createpub-10知识图谱表示学习.pdf Jun9,2019 pub-11知识存储.pdf Createpub-11知识存储.pdf Jun9,2019 pub-12知识问答-微软小冰.pdf Createpub-12知识问答-微软小冰.pdf Jun9,2019 pub-13实体链接.pdf Createpub-13实体链接.pdf Jun9,2019 pub-14知识推理.pdf Createpub-14知识推理.pdf Jun9,2019 pub-1知识图谱概论A.pdf newppt Mar15,2019 pub-1知识图谱概论B.pdf newppt Mar15,2019 pub-1知识图谱概论C.pdf newppt Mar15,2019 pub-2知识表示.pdf newppt Mar15,2019 pub-3知识建模.pdf Createpub-3知识建模.pdf Mar23,2019 pub-4知识抽取-问题和方法.pdf Createpub-4知识抽取-问题和方法.pdf Mar23,2019 pub-5知识抽取-数据获取.pdf Createpub-5知识抽取-数据获取.pdf Mar30,2019 pub-6知识抽取-实体识别.pdf Createpub-6知识抽取-实体识别.pdf Mar30,2019 pub-7知识抽取-关系抽取.pdf Createpub-7知识抽取-关系抽取.pdf Jun9,2019 pub-8知识抽取-事件抽取.pdf Createpub-8知识抽取-事件抽取.pdf Mar30,2019 pub-9知识融合.pdf Createpub-9知识融合.pdf Jun9,2019 Viewcode Asystematiccourseaboutknowledgegraphforgraduatestudents,interestedresearchersandengineers. 课程内容 第1讲知识图谱概论（2019-3-1,2019-3-8） 第2讲知识表示(2019-3-15) 第3讲知识建模（2019-3-15，2019-3-22） 第4讲知识抽取基础:问题和方法（2019-3-22） 第5讲知识抽取:数据采集（2019-3-29） 第6讲知识抽取:实体识别（2019-3-29） 第7讲知识抽取:关系抽取（2019-4-19，2019-4-26） 第8讲知识抽取:事件抽取（2019-3-29） 第9讲知识融合（2019-4-28） 第10讲知识图谱表示学习（2019-5-5） 第11讲知识存储（2019-5-10） 第12讲基于知识的智能问答（2019-5-10） 第13讲实体链接（2019-5-17） 第14讲知识推理（2019-5-17） 附录A：经典文献选读 知识图谱构建 知识表示和建模 知识抽取 知识融合 知识图谱嵌入 知识推理/知识挖掘 实体链接 知识存储/知识查询 人机交互 附录B：最新进展论文选读(近1年内) 附录B：其它资源 README.md Asystematiccourseaboutknowledgegraphforgraduatestudents,interestedresearchersandengineers. 东南大学《知识图谱》研究生课程 时间：春季学期（2月下旬~5月中旬） 地点：东南大学九龙湖校区 授课人：汪鹏 答疑/讨论/建议：请致信pwangATseu.edu.cn 课程内容 第1讲知识图谱概论（2019-3-1,2019-3-8） 1.1知识图谱起源和发展 1.2知识图谱VS深度学习 1.3知识图谱VS关系数据库VS传统专家库 1.4知识图谱本质和核心价值 1.5知识图谱技术体系 1.6典型知识图谱 1.7知识图谱应用场景 课件下载:partApartBpartC 第2讲知识表示(2019-3-15) 2.1知识表示概念 2.2知识表示方法 语义网络 产生式系统 框架系统 概念图 形式化概念分析 描述逻辑 本体 本体语言 统计表示学习 课件下载:partA 第3讲知识建模（2019-3-15，2019-3-22） 3.1本体 3.2知识建模方法 本体工程 本体学习 知识建模工具 知识建模实践 课件下载:partA 第4讲知识抽取基础:问题和方法（2019-3-22） 4.1知识抽取场景 4.2知识抽取挑战 4.3面向结构化数据的知识抽取 4.4面向半结构化数据的知识抽取 4.5面向非机构化数据的知识抽取 课件下载:partA 第5讲知识抽取:数据采集（2019-3-29） 5.1数据采集原理和技术 爬虫原理 请求和响应 多线程并行爬取 反爬机制应对 5.2数据采集实践 百科论坛社交网络等爬取实践 课件下载:partA 第6讲知识抽取:实体识别（2019-3-29） 6.1实体识别基本概念 6.2基于规则和词典的实体识别方法 6.3基于机器学习的实体识别方法 6.4基于深度学习的实体识别方法 6.5基于半监督学习的实体识别方法 6.6基于迁移学习的实体识别方法 6.7基于预训练的实体识别方法 课件下载:partA 第7讲知识抽取:关系抽取（2019-4-19，2019-4-26） 7.1关系基本概念 7.2语义关系 7.3关系抽取的特征 7.4关系抽取数据集 7.5基于监督学习的关系抽取方法 7.6基于无监督学习的关系抽取方法 7.7基于远程监督的关系抽取方法 7.8基于深度学习/强化学习的关系抽取方法 课件下载:partA 第8讲知识抽取:事件抽取（2019-3-29） 8.1事件抽取基本概念 8.2基于规则和模板的事件抽取方法 8.3基于机器学习的事件抽取方法 8.4基于深度学习的事件抽取方法 8.5基于知识库的事件抽取方法 8.6基于强化学习的事件抽取方法 课件下载:partA 第9讲知识融合（2019-4-28） 9.1知识异构 9.2本体匹配 9.3匹配抽取和匹配调谐 9.4实体匹配 9.5大规模实体匹配处理 9.6知识融合应用实例 课件下载:partA 第10讲知识图谱表示学习（2019-5-5） 10.1知识表示学习概念 10.2基于距离的表示学习模型 10.3基于翻译的表示学习模型 10.4基于语义的表示学习模型 10.5融合多源信息的表示学习模型 10.6知识图谱表示学习模型的评测 10.7知识图谱表示学习前沿进展和挑战 课件下载:partA 第11讲知识存储（2019-5-10） 11.1知识存储概念 11.2图数据库管理系统、模型、查询语言 11.3RDF数据库管理系统、模型、查询语言 11.4基于关系型数据库的知识存储 课件下载:partA 第12讲基于知识的智能问答（2019-5-10） 12.1智能问答基础 12.2问题理解 12.3问题求解 12.4基于模板的知识问答方法 12.5基于语义分析的知识问答方法 12.6基于深度学习的知识问答方法 12.7IBMWatson原理和技术剖析 12.8微软小冰的原理和技术剖析 课件下载:partA 第13讲实体链接（2019-5-17） 13.1实体链接基本概念 13.2基于概率生成模型的实体链接方法 13.3基于主题模型的实体链接方法 13.4基于图的实体链接方法 13.5基于深度学习的实体链接方法 13.6基于无监督的实体链接方法 课件下载:partA 第14讲知识推理（2019-5-17） 14.1知识推理基础概念 14.2基于逻辑的知识推理方法 14.3基于统计学习的知识推理方法 14.4基于图的知识推理方法 14.4基于神经网络的知识推理方法 14.5多种方法混合的知识推理方法 课件下载:partA 附录A：经典文献选读 知识图谱构建 DongX,GabrilovichE,HeitzG,etal.Knowledgevault:Aweb-scaleapproachtoprobabilisticknowledgefusion.KDD2014:601-610. 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Cao,Y.,Hou,L.,Li,J.,&Liu,Z.(2018).Neuralcollectiveentitylinking.arXivpreprintarXiv:1811.08603. ---知识融合--- Wang,Zhichun,etal.Cross-lingualknowledgegraphalignmentviagraphconvolutionalnetworks.Proceedingsofthe2018ConferenceonEmpiricalMethodsinNaturalLanguageProcessing.2018. ---知识补全--- XiaolanWang,XinLunaDong,YangLi,AlexandraMeliou.MIDAS:Findingtherightwebsourcestofillknowledgegaps.ICDE2019. ---知识构建--- NguyenDB,AbujabalA,TranNK,etal.Query-drivenon-the-flyknowledgebaseconstruction.VLDB2017. ---知识评估--- GaoJ,LiX,XuYE,etal.Efficientknowledgegraphaccuracyevaluation.VLDB2019,12(11):1679-1691. ---知识图谱：综述、概念和发展--- ClaudioGutierrezandJuanF.Sequeda.ABriefHistoryofKnowledgeGraph'sMainIdeas:Atutorial.ISWC2019tutorial NoyN,GaoY,JainA,etal.Industry-scaleknowledgegraphs:Lessonsandchallenges.ACMQueue,2019,17(2):48-75. 附录B：其它资源 Top-levelConferencePublicationsonKnowledgeGraph(2018-2020) StanfordSpring2020《KnowledgeGraphs》 About 东南大学《知识图谱》研究生课程 Resources Readme Stars 3k stars Watchers 98 watching Forks 961 forks Releases Noreleasespublished Packages0 Nopackagespublished Youcan’tperformthatactionatthistime. Yousignedinwithanothertaborwindow.Reloadtorefreshyoursession. Yousignedoutinanothertaborwindow.Reloadtorefreshyoursession.