个人简介
陈华钧，浙江大学计算机科学与技术学院教授/博导，在WWW/ISWC,IJCAI/AAAI/KR,ACL/EMNLP/NAACL,VLDB/ICDE,IEEE Magazine on Computational Intelligence,IEEE Intelligent System,IEEE TKDE,Briefings in Bioinforamtics,AI in Medicine等国际顶级会议或期刊上发表多篇论文。作为负责人主持2项国家自然科学基金重点类项目，以及国家重点研发计划课题、国家重大科技专项项目及企业合作项目等二十余项。曾获国际语义网会议ISWC2006最佳论文奖（第一作者）、教育部技术发明一等奖、国家科技进步二等奖、中国中文信息学会钱伟长科技奖一等奖、阿里巴巴优秀学术合作奖、博文视点图书奖等奖励。
本实验室长期招聘优秀博士后及全职高素质研究人员，并提供有吸引力的待遇及发展空间，有意者烦请邮件联系。
奖励荣誉
国际语义网大会ISWC最佳论文奖（2006，第一作者）
教育部技术发明一等奖（2013）：基于语义图的知识服务平台及中医药应用
国家科技进步二等奖（2010）
中国中文信息学会钱伟长中文信息处理科技奖一等奖（2020）：千亿级商品知识图谱构建及应用
阿里巴巴优秀学术合作奖（2019）：藏经阁—知识引擎—知识推理
博文视点最具震撼力图书大奖（2019）/中国工信出版传媒集团优秀出版物一等奖（2020）：《知识图谱：方法、技术与实践》
中国计算机大会十佳优秀论坛/YOCESF年度优秀报告会(2018)：知识图谱赋能数字经济
部分在研课题
国家自然科学基金重点项目（大数据重大研究计划）：面向商务大数据的知识图谱引擎关键技术研究，2019-2022，项目负责人
国家自然科学基金重点项目（联合基金）：面向复杂推理的可解释知识图谱技术及在政府治理领域的应用，2020-2023，项目负责人
科技部国家重点研发计划项目：面向复杂动态环境下的知识图谱关键技术研究，2020-2023,课题负责人
阿里巴巴创新研究计划：商品知识图谱与藏经阁知识引擎平台，2015-？，项目负责人
学术报告
2021 Keynote Speaker,6th IEEE International Conference on Big Data Analytics:Reasoning with Knowledge Graph for Big Data Analytics.
2020 CNCC中国计算机大会中文信息技术战略研讨会：知识图谱与低资源学习
2020哈尔滨工业大学人工智能研究院“认知智能论坛”/阿里巴巴“知识图谱及在人机对话场景中的应用论坛”邀请报告：知识图谱技术前沿：可微推理、可解释性与低资源
2019中德科学中心“融合符号表示与数值表示的常识推理”中德国际研讨会：Differentiable Reasoning with Knowledge Graphs:Explanations and Low Resources
2019 OpenKG Special Forum at JIST2019:OpenKG BlockChain
2019 CCF中国信息系统大会特邀报告/CNCC中国计算机大会知识图谱论坛报告：可解释的知识图谱推理及应用
2019阿里巴巴Tech面对面“可解释AI的技术解谜”暨“藏经阁产品发布会”主题报告：可解释的知识图谱推理及电商应用
2018华为知识图谱专家论坛报告/中国大数据技术大会知识图谱论坛报告：浅谈知识图谱技术发展前沿
2018云栖大会学术专场：知识引擎：机遇与挑战（阿里巴巴藏经阁研究计划进展报告）
2017 CNCC中国计算机大会知识图谱论坛：OpenKG与cnSchema
2017 OMAHA开放医疗与健康联盟大会：开放的知识图谱
2016深圳机器人&人工智能论坛：语义网、知识图谱与人工智能
2015第三届中文知识图谱研讨会：从数据互联到万物互联（From Linked Data to Linked Everythings）
学术活动
大会主席:CCKS2020全国知识图谱与语义计算大会.
Steering Comittee:The 10th Joint International Conference on Knowledge Graph.JIST-KG2020
General Chair:The 9th Joint International Semantic Technology Conference.JIST2019.
Co-Organizer:First International Workshop on Knowledge Graph Technology and Applications at WWW2019
论坛主席：CNCC2018中国计算机大会专题论坛—知识图谱赋能数字经济
程序委员会主席：CCKS2016全国知识图谱与语义计算大会
Co-Chair:WWW2015 Workshop on Web Data Science and Smart Cities
程序委员会主席:国际语义技术联合会议JIST2011/亚洲语义互联网会议ASWC2011
General Vice-Chair:IEEE/ACM CPSCom2010(Cyber-Physical-Social Computing)
Demo Chair:国际语义互联网会议ISWC2010
Co-Chair:AAAI2008 Workshop on Semantic e-Science
Co-Chair:WWW2007/2008 Workshop on Semantic Web for Healthcares and Life Sciences
Guest Editor:IEEE Computational Intelligence Magazine(IF=3.329),Special Issue on“Semantic Web meets Computational Intelligence”.2012.
Guest Editor:Journal of Biomedical Informatics(IF=2.605).Special Issue on“Semantic Bio-Mashup”.2008.
Guest Editor:BMC Bioinformatics(IF=4.221).Special Issue on“Semantic e-Science for Biomedicine”.2007.
课程简介
知识图谱的早期理念源于万维网之父Tim Berners Lee关于语义网(The Semantic Web)的设想，旨在采用图的结构(Graph Structure)来建模和记录世界万物之间的关联关系和知识，以便有效实现更加精准的对象级搜索。经过近二十年的发展，知识图谱的相关技术已经在搜索引擎、智能问答、语言及视觉理解、大数据决策分析、智能设备物联等众多领域得到广泛应用，被公认为是实现认知智能的重要基石。近年来，随着自然语言处理、深度学习、图数据处理等众多领域的飞速发展，知识图谱在自动化知识获取、基于知识的自然语言处理、基于表示学习的机器推理、基于图神经网络的图挖掘与分析等领域又取得了很多新进展。
本课程是面向浙江大学研究生开设的专业选修课程。课程系统性介绍知识图谱的基本概念、核心技术内涵和应用实践方法，具体内容涉及知识表示与推理、图数据库、关系抽取与知识图谱构建、知识图谱表示学习与嵌入、语义搜索与知识问答、图神经网络与图挖掘分析等。课程内容的设计以“基础、前沿与实践”相结合为基本原则，既包括基本概念介绍和实践应用内容，也包括学术界的最新前沿进展的介绍。
前置课程与知识
建议预先修读数据库、机器学习、自然语言处理等课程，熟悉Tensorflow、Pytorch等常用深度学习框架。
参考教材
《知识图谱：方法、实践与应用》，电子工业出版社，2019
课程内容
Date Descriptons
Suggested Readings
Nov.15
第一讲：知识图谱概述
Lecture 00、
Lecture 01
知识图谱的系统工程观（2018）
Industry-Scale Knowledge Graphs:Lessons and Challenges（2019）CCCF译文|工业级知识图谱：经验与挑战
The Semantic Web（2001）
Nov.22
第二讲：知识图谱的表示与建模
Lecture 02
Tutorials&Tools:Protégé
Sample codes:TransE(preview)DistMult
What is a Knoweldge Representation.AI Magazine(1993)
知识图谱-浅谈RDF、OWL、SPARQL
知识表示学习研究进展.计算机研究与发展（2016）
Knowledge Graph Embedding:A Survey of Approaches and Applications.TKDE(2017)
A Description Logic Primer.(2013)
Nov.29
第三讲：知识图谱的存储与查询
Lecture 03
Tutorials&Tools：Neo4j(Sampledata)
gStore、Jena
知识图谱数据管理研究综述.软件学报.（2019）
数据库视角下的知识图谱研究.CCKS2019顶会Review(2019)
RDF data storage and query processing schemes:A survey.ACM Computing Surveys(2018)
Foundations of modern query languages for graph databases.ACM Computing Surveys(2016)
第四讲：知识图谱的获取与抽取
Lecture 04
Tutorials&Tools:
DeepKE,Deepdive
Sample codes:
CNN/PCNN,GCN,BERT
Semantic Relation Extraction from Text.CCKS2018 Tutorial
Relation Extraction:A Survey(2017)
Relation Extraction Using Distant Supervision:A Survey ACM Computing Surveys(2019)
知识图谱从哪里来：实体关系抽取的现状与未来(2019)
Matching the Blanks:Distributional Similarity for Relation Learning(ACL2019)
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks(NAACL2019)
Simple BERT Models for Relation Extraction and Semantic Role Labeling(2019)
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction(EMNLP2018)
第五讲：知识图谱与机器推理
Lecture 05
Tutorials&Tools:Jena,Drools
Sample codes:AMIE,ANALOGY,ComplEx
面向知识图谱的知识推理研究进展.软件学报（2018）
A Review of Relational Machine Learning for Knowledge Graphs.(Procedding of IEEE 2015)
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning.(WWW2019)
DeepPath:A Reinforcement Learning Method for Knowledge Graph Reasoning.(EMNLP2017)
Fast rule mining in ontological knowledge bases with AMIE+.(VLDBJ 2015)
Differentiable Learning of Logical Rules for Knowledge Base Reasoning.(NIPS2017)
Knowledge Representation and Reasoning on the Semantic Web:OWL.(2011)
(Advanced)Representing Ontologies Using Description Logics,Description Graphs,and Rules.(Artificial Intelligence.2009)
第六讲：知识图谱与智能问答
Lecture 06
Tutorials&Tools:gAnswer
《智能问答》.高等教育出版社（2018）
Semantic Parsing via Staged Query Graph Generation:Question Answering with Knowledge Base.(ACL2015)
Improved Neural Relation Detection for Knowledge Base Question Answering.(ACL2017)
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs.(2019)
Go for a Walk and Arrive at the Answer:Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.(ICLR2018)
UHop:An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering.(NAACL2019)
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader.(ACL2019)
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering.(NAACL2019)
第七讲：知识图谱与图网络算法
Lecture 07
Sample codes:Deepwalk,GCN,GAT
Representation Learning on Networks.WWW2019 Tutorials
Deep Learning for Graphs.CCKS2019 Tutorials
Graph Neural Networks:A Review of Methods and Applications
Inductive Representation Learning on Large Graphs.NIPS2017
Deep Graph Infomax.ICLR2019
Heterogeneous Graph Attention Network.WWW2019
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion.AAAI2019
On the Equivalence between Node Embeddings and Structural Graph Representations.ICLR2020
第八讲：知识图谱新发展和新应用
Lecture 08
AZFT知识引擎实验室OpenKGBig Data Research
AZFT知识引擎实验室依托浙江大学计算机科学与技术学院、浙江大学软件学院、阿里巴巴新零售技术事业群、阿里达摩院等多个平台资源支持，团队负责人为浙江大学计算机科学与技术学院陈华钧教授。联合实验室重点围绕知识图谱及自然语言处理相关方向开展前沿学术研究和技术研发，包括知识图谱推理与表示学习、敏捷知识图谱构建与抽取、知识驱动的可解释性人工智能、图神经网络与图谱挖掘分析、知识驱动的自然语言处理、多模态语义理解等。同时，联合实验室作为阿里巴巴藏经阁知识引擎的主要支持团队，也致力于推动知识图谱领域的产学研合作，以及在工业界和多个垂直领域的落地应用，这包括：阿里商品知识图谱、电子政务图谱、金融知识图谱、智能生物医药等领域应用。此外，实验室还积极致力于推动知识图谱的开源开放工作，是中文开放知识图谱OpenKG的核心发起及支持团队。诚邀有志于推动知识图谱前沿学术进步、产业化落地应用和知识图谱开源开放的青年人才加盟。
OpenKG是中国中文信息学会语言与知识计算专业委员会所倡导，由来自浙江大学、东南大学、同济大学等多个单位的知识图谱团队共同维护的开放知识图谱社区项目。
近期论文
查看导师最新文章
（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）
Jiaoyan Chen,Freddy Lecue,Jeff Z.Pan and Huajun Chen.Knowledge Graph Embeddings for Dealing with Concept Drift in Machine Learning.Journal of Web Semantics.(JWS 2021)
Yuxia Geng,Jiaoyan Chen,Zhuo Chen,Jeff Z.Pan,Zhiquan Ye,Zonggang Yuan,Yantao Jia and Huajun Chen*.OntoZSL:Ontology-enhanced Zero-shot Learning.TheWebConference.WWW2021.（CCF A）
Wen Zhang,Ganqiang Ye,Bo Wen,Wei Zhang,Huajun Chen*.Billion-scale Pre-trained E-commerce Knowledge Graph Model.ICDE2021.（CCF A）
Chi-Man Wong,Fan Feng,Wen Zhang,Chi-Man Vong,Hui Chen,Yichi Zhang,Peng He,Huan Chen,Kun Zhao,Huajun Chen*.Improving Conversational Recommender System by Pretraining Billion-scale Knowledge Graph.ICDE2021.（CCF A）
Hongbin Ye,Ningyu Zhang,Mosha Chen,Fei Huang,Huajun Chen*.Contrastive Triple Extraction with Generative Transformer.AAAI 2021.（CCF A）
陈华钧、张伟、楚巍.可微的知识图谱推理及在电商和金融领域的应用.中国人工智能学会通讯-知识工程专题,CCAI(2020第九期)
陈华钧、张宁豫、陈名扬等.低资源条件下的知识图谱推理与构建.中国计算机学会通讯-知识图谱专题.CCCF(2020年第八期).
Yuxia Geng,Jiaoyan Chen,Zhiquan Ye,Wei Zhang,Huajun Chen*:Explainable Zero-shot Learning via Attentive Graph Convolutional Network and Knowledge Graph.Semantic Web Journal(IF=3.524).2020
Zhiyuan Ye,Yuxia Geng,Jiaoyan Chen,Jinmin Chen,Huajun Chen*.Zero-shot Text Classification via Reinforced Self-training.Annual Conference of the Association for Computational Linguistics.ACL2020.（CCF A）
Junyou Li,Qingxia Liu,Gong Cheng,Wen Zhang,Evgeny Kharlamov,Kalpa Gunaratna,Huajun Chen.Neural Entity Summarization with Joint Encoding and Weak Supervision.The 29th International Joint Conference on Artificial Intelligence,IJCAI2020.（CCF A）
Ningyu Zhang,Shumin Deng,Wei Zhang,and Huajun Chen*.Relation Adversarial Network for Low Resource Knowledge Graph Completion.WWW2020.（CCF A）
Jiaojian Kang,Wen Zhang,Wei Zhang,Huajun Chen*.Learning Rule Embeddings over Knowledge Graphs:A Case Study from E-Commerce Entity Alignment.TheWebConference.WWW2020.（CCF A）
Shumin Deng;Ningyu Zhang;Zhanlin Sun;Jiaoyan Chen;Wei Zhang;Huajun Chen*,When Low Resource NLP Meets Unsupervised Language Model:Meta-pretraining Then Meta-learning for Few-shot Text Classification,AAAI 2020.（CCF A）
Haiyang Yu,Ningyu Zhang,Shumin Deng,Hongbin Ye,Wei Zhang,Huajun Chen*.Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction.The 28th International Conference on Computational Linguistics.COLING2020
Juan Li,Ruoxu Wang,Ningyu Zhang,Wen Zhang,Fan Yang,Huajun Chen*.Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification.The 28th International Conference on Computational Linguistics.COLING2020.
Ningyu Zhang,Shumin Deng,Juan Li,Xi Chen,Wei Zhang,Huajun Chen*.Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention.International Conference on Empirical Methods in Natural Language Processing.EMNLP2020.
Ningyu Zhang,Shumin Deng,Zhen Bi,Haiyang Yu,Jiacheng Yang,Mosha Chen,Fei Huang,Wei Zhang,Huajun Chen*.OpenUE:An Open Toolkit of Universal Extraction from Text.International Conference on Empirical Methods in Natural Language Processing .EMNLP2020.
Jiaoyan Chen,Freddy Lecue,Yuxia Geng,Jeff Z.Pan and Huajun Chen.Ontology-guided Semantic Composition for Zero-shot Learning.17th International Conference on Principles of Knowledge Representation and Reasoning.KR2020
Shumin Deng,Ningyu Zhang,Wei Zhang,and Huajun Chen*.Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection.ACM International Conference on Web Search and Data Mining.WSDM 2020.
Mingyang Chen,Wen Zhang,Wei Zhang,Qiang Chen and Huajun Chen*.Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs.International Conference on Empirical Methods in Natural Language Processing,EMNLP2019.
Jiaoyan Chen,Freddy Lécué,Jeff Z.Pan,and Huajun Chen.Augmenting Transfer Learning with Semantic Reasoning.28th International Joint Conference on Artificial Intelligence.IJCAI2019.（CCF A）
Wen Zhang,Bibek Paudel,Jiaoyan Chen,Wei Zhang,Abraham Bernstein,Huajun Chen*.Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning.TheWebConference.WWW2019 Research Track.（CCF A）
Wen Zhang,Bibek Paudel,Wei Zhang,Abraham Bernstein,Huajun Chen*.Interaction Embeddings for Prediction and Explanation in Knowledge Graphs.ACM International Conference on Web Search and Data Mining.WSDM2019:96-104
Ningyu Zhang,Shumin Deng,Zhanlin Sun,Guanying Wang,Xi Chen,Wei Zhang and Huajun Chen*.Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.NAACL2019 Research Track.
Shumin Deng,Ningyu Zhang,Wen Zhang,Jiaoyan Chen,Jeff Z.Pan,and Huajun Chen*.Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolution Network. First International Workshop on Knowledge Graph Technologies and Applications at WWW 2019.
Yuxia Geng,Jiaoyan Chen,Ernesto Jiménez-Ruiz,Huajun Chen.Human-centric Transfer Learning Explanation via Knowledge Graph.AAAI 2019 Workshop on Network Interpretability for Deep Learning(2019).
Ningyu Zhang,Shumin Deng,Zhanling Sun,Xi Chen,Wei Zhang,Huajun Chen*:Attention-Based Capsule Network with Dynamic Routing for Relation Extraction.EMNLP 2018:986-992
Guanying Wang,Wen Zhang,Ruoxu Wang,Yalin Zhou,Xi Chen,Wei Zhang,Hai Zhu,Huajun Chen*.Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding.EMNLP 2018:2246-2255
Jiaoyan Chen,Freddy Lécué,Jeff Z.Pan,Ian Horrocks,Huajun Chen*.Knowledge-Based Transfer Learning Explanation.Principles of Knowledge Representation and Reasoning:KR2018:349-358
Matthias Baumgartner,Wen Zhang,Bibek Paudel,Daniele Dell'Aglio,Huajun Chen*,Abraham Bernstein.Aligning Knowledge Base and Document Embedding Models Using Regularized Multi-Task Learning.International Semantic Web Conference(1)2018:21-37
Jiaoyan Chen,Freddy Lécué,Jeff Z.Pan,Huajun Chen*:Learning from Ontology Streams with Semantic Concept Drift.IJCAI 2017:957-963（CCF A）
(Book)于彤、陈华钧、姜晓红中医药知识工程.科学出版社第1版2017年1月.ISBN:9787030518194
(Book)Huajun Chen,Heng Ji,Le Sun,Haixun Wang,Tieyun Qian,Tong Ruan:Knowledge Graph and Semantic Computing:Semantic,Knowledge,and Linked Big Data.CCKS 2016 Revised Selected Papers.Springer Publisher,ISBN 9789811031670
Ningyu Zhang,Huajun Chen*,Xi Chen,Jiaoyan Chen:Semantic Framework of Internet of Things for Smart Cities:Case Studies.Sensors 16(9):1501(2016)
Jiaoyan Chen,Huajun Chen*,Jeff Z.Pan:Semantic Reasoning for Smog Disaster Analysis.Description Logics 2016
Xi Chen,Huajun Chen*,Ningyu Zhang,Jue Huang,Wen Zhang:Large-Scale Real-Time Semantic Processing Framework for Internet of Things.International Journal of Distributed Sensor Networks.11:365372:1-365372:11(2015)(ESI高引论文)
Peiqin Gu,Huajun Chen*:Modern bioinformatics meets traditional Chinese medicine.Briefings in Bioinformatics 15(6):984-1003(2014)(中科院一区)
Huajun Chen,Tong Yu,Jake Y.Chen:Semantic Web meets Integrative Biology:a survey.Briefings in Bioinformatics 14(1):109-125(2013)(IF=9.101)(中科院一区)
(Book)Zhaohui Wu,Huajun Chen,Xiaohong Jiang.Modern Computational Approaches to Tradtional Chinese Medicine.Eslevier Publisher.2013.
Huajun Chen,Zhaohui Wu,Philippe Cudré-Mauroux:Semantic Web Meets Computational Intelligence:State of the Art and Perspectives.IEEE Comp.Int.Mag.7(2):67-74(2012)(IF=5.857)
Matthias Samwald,Huajun Chen,Alan Ruttenberg,Ernest Lim,Luis N.Marenco,Perry L.Miller,Gordon M.Shepherd,Kei-Hoi Cheung:Semantic SenseLab:Implementing the vision of the Semantic Web in neuroscience.Artificial Intelligence in Medicine 48(1):21-28(2010)(IF=3.574)
Huajun Chen,Li Ding,Zhaohui Wu,Tong Yu,Lavanya Dhanapalan,Jake Yue Chen:Semantic web for integrated network analysis in biomedicine.Briefings in Bioinformatics 10(2):177-192(2009)(IF=9.101)(中科院一区)
Zhaohui Wu,Yuxin Mao,Huajun Chen:Subontology-Based Resource Management for Web-Based e-Learning.IEEE Trans.Knowl.Data Eng.21(6):867-880(2009)（CCF A）
Zhaohui Wu,Tong Yu,Huajun Chen*,Xiaohong Jiang,Chunying Zhou,Yu Zhang,Yuxin Mao,Yi Feng,Meng Cui,Aining Yin:Semantic Web Development for Traditional Chinese Medicine.AAAI/IAAI 2008:1757-1762（CCF A）
Huajun Chen,Zhaohui Wu,Heng Wang,Yuxin Mao:RDF/RDFS-based Relational Database Integration.ICDE 2006:94-106（CCF A）
Huajun Chen,Yimin Wang,Heng Wang,Yuxin Mao,Jinmin Tang,Chunying Zhou,Aining Yin,Zhaohui Wu:Towards a Semantic Web of Relational Databases:A Practical Semantic Toolkit and an In-Use Case from Traditional Chinese Medicine.International Semantic Web Conference 2006:750-763.(Best Paper Award)
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