npubird/KnowledgeGraphCourse

A systematic course about knowledge graph for graduate students, interested researchers and engineers.

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 pwang AT seu.edu.cn

课程内容

第0讲 课程介绍（2025-02-21）

课件下载:2025-pub-0课程介绍.pdf

第1讲 知识图谱概论 （2025-02-21，02-28，03-07）

1.1 认知智能的知识图谱视角浅析

• 知识视角的认知智能
• 知识图谱本质
• 知识图谱的演化和形成过程
• 知识图谱 VS 深度学习
• 知识图谱 VS 传统知识库 VS 数据库
• 知识图谱应用场景
• 知识图谱本质和核心价值

1.2 知识图谱的技术体系剖析

• 知识抽取
• 知识融合
• 知识表示学习
• 知识推理
• 知识存储

1.3 知识图谱的瓶颈、问题和挑战的思考

• 知识获取挑战
• 知识质量挑战
• 知识的智能应用挑战

课件下载: 2025-partA
2025-partB

第2讲 知识表示 (2025-03-07)

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
• 形式概念分析
• 描述逻辑
• 本体
• 本体语言
• 知识图谱表示学习

课件下载:2025-pub-2知识表示.pdf

第3讲 知识建模 （2025-03-14）

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:2025-pub-3知识建模.pdf

第4讲 知识抽取基础:问题和方法（2025-03-14）

4.1 问题分析

• 知识抽取场景
• 知识抽取挑战

4.2 知识抽取场景和方法

• 面向结构化数据的知识抽取
• 面向半结构化数据的知识抽取
• 面向无结构化数据的知识抽取

课件下载:2025-pub-4知识抽取问题与方法.pdf

第5讲 知识抽取:实体识别（2025-03-21）

5.1 实体识别基本概念
5.2 基于规则和词典的实体识别方法
5.3 基于机器学习的实体识别方法
5.4 基于深度学习的实体识别方法
5.5 基于半监督学习的实体识别方法
5.6 基于迁移学习的实体识别方法
5.7 基于预训练的实体识别方法
5.8 实体识别研究前沿进展

课件下载:2025-pub-5知识抽取-实体识别.pdf
课件下载:2025-pub-5.1实体识别研究前沿进展2023.pdf

第6讲 知识抽取:关系抽取（2025-03-28）

6.1 关系抽取简介
6.2 语义关系
6.3 关系抽取中的特征
6.4 关系抽取数据集
6.5 基于模板的关系抽取
6.6 有监督实体关系抽取
6.7 弱监督实体关系抽取
6.8 远程监督实体关系抽取
6.9 无监督实体关系抽取
6.10 基于深度学习/强化学习的关系抽取

课件下载:2025-pub-6知识抽取-关系抽取.pdf

第7讲 知识抽取:事件抽取（2025-04-11）

7.1 事件抽取基本概念
7.2 事件抽取方法

• 基于规则和模板的事件抽取方法
• 基于机器学习的事件抽取方法
• 基于深度学习的事件抽取方法
• 基于知识库的事件抽取方法

7.3 金融领域事件抽取系统实现
7.4 事理图谱的研究与应用
7.5 从经典到大模型的范式

课件下载:2025-pub-7知识抽取-事件抽取.pdf
课件下载:2025-pub-7-1 知识抽取-事件抽取.pdf

第8讲 知识融合（2025-04-18）

8.1 知识异构
8.2 本体匹配
8.3 匹配抽取和匹配调谐
8.4 实例匹配
8.5 大规模实体匹配处理
8.6 知识融合应用实例

课件下载:2025-pub-8 知识融合.pdf

第9讲 知识图谱与大语言模型前沿进展（2025-04-25）

9.1 KG for LLM

• Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-Based Retrofitting
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
• OneKE
• Graph Retrieval-Augmented Generation: A Survey
• Knowledge Graph-Enhanced Large Language Models via Path Selection
• Knowledge Graph Enhanced Large Language Model Editing
• Enhancing Large Language Models with Knowledge Graphs for Robust Question Answering
• OntoFact: Unveiling Fantastic Fact-Skeleton of LLMs via Ontology-Driven Reinforcement Learning
• Unveiling factuality and injecting knowledge for LLMs via reinforcement learning and data proportion

9.2 LLM for KG

• Unifying Large Language Models and Knowledge Graphs: A Roadmap
• ConsistNER: Towards Instructive NER Demonstrations for LLMs with the Consistency of Ontology and Context
• InstructNER: Boosting Textural NER with Synthetic Image and Instructive Alignment
• Boosting LLMs With Ontology-Aware Prompt For NER Data Augmentation
• Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
• KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models

课件下载:2025-pub-9 知识图谱与大模型前沿进展.pdf

第0讲 课程介绍 （2024-02-22）

课件下载:2024-pub-0课程介绍.pdf

第1讲 知识图谱概论 （2024-02-29，03-08，03-14）

1.1 认知智能的知识图谱视角浅析

• 知识视角的认知智能
• 知识图谱本质
• 知识图谱的演化和形成过程
• 知识图谱 VS 深度学习
• 知识图谱 VS 传统知识库 VS 数据库
• 知识图谱应用场景
• 知识图谱本质和核心价值

1.2 知识图谱的技术体系剖析

• 知识抽取
• 知识融合
• 知识表示学习
• 知识推理
• 知识存储

1.3 知识图谱的瓶颈、问题和挑战的思考

• 知识获取挑战
• 知识质量挑战
• 知识的智能应用挑战

课件下载: 2024-partA
2024-partB
2024-partC

第2讲 知识表示 (2024-03-21)

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
• 形式概念分析
• 描述逻辑
• 本体
• 本体语言
• 知识图谱表示学习

课件下载:2024-pub-2知识表示.pdf

第3讲 知识建模 （2024-03-21）

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:2024-pub-3知识建模.pdf

第4讲 知识抽取基础:问题和方法（2024-03-28）

4.1 问题分析

• 知识抽取场景
• 知识抽取挑战

4.2 知识抽取场景和方法

• 面向结构化数据的知识抽取
• 面向半结构化数据的知识抽取
• 面向无结构化数据的知识抽取

课件下载:2024-pub-4知识抽取问题与方法.pdf

第5讲 知识抽取:实体识别（2024-03-28）

5.1 实体识别基本概念
5.2 基于规则和词典的实体识别方法
5.3 基于机器学习的实体识别方法
5.4 基于深度学习的实体识别方法
5.5 基于半监督学习的实体识别方法
5.6 基于迁移学习的实体识别方法
5.7 基于预训练的实体识别方法
5.8 实体识别研究前沿进展
5.9 从经典到大模型的范式

课件下载:2024-pub-5知识抽取-实体识别.pdf
课件下载:2024-pub-5知识抽取-从经典到大模型的范式.pdf 课件下载:2024-pub-5.1实体识别研究前沿进展2023.pdf

第6讲 知识抽取:关系抽取（2024-04-11）

6.1 关系抽取简介
6.2 语义关系
6.3 关系抽取中的特征
6.4 关系抽取数据集
6.5 基于模板的关系抽取
6.6 有监督实体关系抽取
6.7 弱监督实体关系抽取
6.8 远程监督实体关系抽取
6.9 无监督实体关系抽取
6.10 基于深度学习/强化学习的关系抽取

课件下载:2024-pub-6知识抽取-关系抽取.pdf

第7讲 知识抽取:事件抽取（2024-04-25）

7.1 事件抽取基本概念
7.2 事件抽取方法

• 基于规则和模板的事件抽取方法
• 基于机器学习的事件抽取方法
• 基于深度学习的事件抽取方法
• 基于知识库的事件抽取方法

7.3 金融领域事件抽取系统实现
7.4 事理图谱的研究与应用

课件下载:2024-pub-7知识抽取-事件抽取.pdf

第8讲 知识融合（2024-04-25）

8.1 知识异构
8.2 本体匹配
8.3 匹配抽取和匹配调谐
8.4 实例匹配
8.5 大规模实体匹配处理
8.6 知识融合应用实例

课件下载:2024-pub-8知识融合.pdf
课件下载:2024-pub-8知识融合前沿进展.pdf

第9讲 知识图谱表示学习（2024-04-28）

9.1 知识表示学习原理及概念
9.2 知识表示学习方法

• 基于翻译的表示学习模型
• 基于语义匹配的表示学习模型
• 融合多源信息的表示学习模型

9.3 知识图谱表示学习模型的评测
9.4 知识图谱表示学习前沿进展和挑战

课件下载:2024-pub-9知识表示学习

2024-第10讲 KG+LLM论文分享（2024-04-25，2024-04-28）

10.1 LLM for KG

• (KGC)KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph
• (KGC)Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
• (Reasoning)KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
• (Reasoning)StructGPT: A General Framework for Large Language Model to Reason over Structured Data
• (Reasoning)Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
• (RE)Revisiting Large Language Models as Zero-shot Relation Extractors
• (RE)Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
• (NER)ConsistNER: Towards Instructive NER Demonstrations for LLMs with the Consistency of Ontology and Context
• (NER)InstructNER: Boosting Textural NER with Synthetic Image and Instructive Alignment
• (NER)Boosting LLMs with Ontology-Aware Prompt for NER Data Augmentation

10.2 KG for LLM

• Systematic Assessment of Factual Knowledge in Large Language Models
• OntoFact: Unveiling Fantastic Fact-Skeleton of LLMs via Ontology-Driven Reinforcement Learning
• Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
• When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
• Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

10.3 KG vs LLM

• LLMs4OL: Large Language Models‌ for Ontology Learning
• DeepOnto: A Python Package for Ontology Engineering with Deep Learning
• Do PLMs Know and Understand Ontological Knowledge?
• Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of GPT family LLMs’ Question Answering Performance

课件下载:2024-KG+LLM论文分享

2023-第10讲 ChatGPT到KG（2023）

10.1 ChatGPT简介
10.2 ChatGPT关键技术
10.3 ChatGPT展望
10.4 KGCODE团队研究

2023-第11讲 ChatGPT相关论文分享（2023-5-5）

11.1 Prompt简介及其在数据增强方面的应用

• Prompt背景介绍
• 什么是Prompt
• Prompt构建
• Prompt数据增强

11.2 ChatGPT在质量评测和Prompt工程方面的应用

• 论文：通过问题生成和问答评价知识基础对话中的事实一致性
• 论文：利用外部知识和自动反馈改进大型语言模型
• 论文：大语言模型的主动提示与思维链
• 论文：渐进式提示提高大语言模型的推理能力

课件下载:pub-11Prompt分享.pdf
课件下载:pub-11ChatGPT在质量评测和Prompt工程方面的应用.pdf
课件下载百度云链接:pub-11Prompt分享.pdf 提取码：kgkg
课件下载百度云链接:pub-11ChatGPT在质量评测和Prompt工程方面的应用.pdf 提取码：kgkg

第12讲 ChatGPT相关论文分享（2023-5-12）

12.1 ChatGPT在信息抽取和多模态方面的应用

• 论文：大型语言模型不是一个好的少样本信息抽取工具，但对于困难样本是一个好的重排工具
• 论文：Multimodal Chain-of-Thought Reasoning in Language Models
• 论文：Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

课件下载:pub-12ChatGPT在信息抽取和多模态方面的应用.pdf
课件下载百度云链接:pub-12ChatGPT在信息抽取和多模态方面的应用.pdf 提取码：kgkg

附录A：经典文献选读

知识图谱构建

1. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. KDD2014: 601-610.
2. Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge. WWW2007: 697-706.
3. Hoffart J, Suchanek F M, Berberich K, et al. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence, 2013, 194: 28-61.
4. Navigli R, Ponzetto S P. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 2012, 193: 217-250.
5. Auer S, Bizer C, Kobilarov G, et al. Dbpedia: A nucleus for a web of open data. ISWC2007: 722-735.
6. Mitchell T, Cohen W, Hruschka E, et al. Never-ending learning. Communications of the ACM, 2018, 61(5): 103-115. earlier work
7. Nguyen, Dat Ba, et al. Query-driven on-the-fly knowledge base construction. Proceedings of the VLDB Endowment 11.1 (2017): 66-79.
8. Speer, Robert, Joshua Chin, and Catherine Havasi. Conceptnet 5.5: An open multilingual graph of general knowledge. AAAI2017.

知识表示和建模

1. Sowa J F. Knowledge representation: logical, philosophical, and computational foundations. 1999.
2. Noy N F, McGuinness D L. Ontology Development 101: A Guide to Creating Your First Ontology. another version

知识抽取

• 信息抽取

1. Etzioni O, Cafarella M, Downey D, et al. Web-scale information extraction in knowitall:(preliminary results).WWW2004: 100-110.
2. Banko M, Cafarella M J, Soderland S, et al. Open information extraction from the web. IJCAI2007, 7: 2670-2676.
3. Sarawagi S. Information extraction. Foundations and Trends® in Databases, 2008, 1(3): 261-377.
4. Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. EMNLP2011: 1535-1545.
5. Fan J, Kalyanpur A, Gondek D C, et al. Automatic knowledge extraction from documents. IBM Journal of Research and Development, 2012, 56(3.4): 5: 1-5: 10.
6. Hearst M A. Automatic acquisition of hyponyms from large text corpora. ACL1992: 539-545.

• 实体识别

1. Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investigationes, 2007, 30(1): 3-26.
2. Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition. NAACL-HLT 2016.
3. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991, 2015.
4. Alhelbawy A, Gaizauskas R. Graph ranking for collective named entity disambiguation. ACL2014, 2: 75-80.
5. Florian R, Ittycheriah A, Jing H, et al. Named entity recognition through classifier combination. HLT-NAACL2003: 168-171.
6. Chiu J P C, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 2016, 4: 357-370.
7. Nothman J, Ringland N, Radford W, et al. Learning multilingual named entity recognition from Wikipedia. Artificial Intelligence, 2013, 194: 151-175.
8. Santos C N, Guimaraes V. Boosting named entity recognition with neural character embeddings. Proceedings of NEWS 2015 The Fifth Named Entities Workshop, 2015.
9. Chiticariu L, Krishnamurthy R, Li Y, et al. Domain adaptation of rule-based annotators for named-entity recognition tasks. EMNLP2010: 1002-1012.
10. Shaalan K. A survey of arabic named entity recognition and classification. Computational Linguistics, 2014, 40(2): 469-510.
11. Speck R, Ngomo A C N. Ensemble learning for named entity recognition. ISWC2014:519-534.
12. Habibi M, Weber L, Neves M, et al. Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics, 2017, 33(14): i37-i48.

• 关系抽取

1. Wang C, Kalyanpur A, Fan J, et al. Relation extraction and scoring in DeepQA. IBM Journal of Research and Development, 2012, 56(3.4): 9: 1-9: 12.

2. Socher R, Huval B, Manning C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//EMNLP, 2012: 1201-1211.

3. Liu C Y, Sun W B, Chao W H, et al. Convolution neural network for relation extraction[C]//International Conference on Advanced Data Mining and Applications. Springer, Berlin, Heidelberg, 2013: 231-242.

4. Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[J]. 2014.

5. Santos, Cicero Nogueira dos, Bing Xiang, and Bowen Zhou. “Classifying relations by ranking with convolutional neural networks.” In Proceedings of ACL, 2015.

6. Zeng D, Liu K, Chen Y, et al. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks[C]//Emnlp. 2015: 1753-1762.

7. Miwa M , Bansal M . End-to-end Relation Extraction using LSTMs on Sequences and Tree Structures[J]. ACL, 2016: 1105–1116.

8. Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2016, 2: 207-212.

9. Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2124-2133.

10. Cai R, Zhang X, Wang H. Bidirectional recurrent convolutional neural network for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 756-765.

11. Wang L, Cao Z, De Melo G, et al. Relation classification via multi-level attention cnns[J]. 2016.

12. Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2016, 2: 207-212.

13. Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2124-2133.

14. Lin Y, Liu Z, Sun M. Neural relation extraction with multi-lingual attention[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017: 34-43.

15. Huang Y Y, Wang W Y. Deep residual learning for weakly-supervised relation extraction[J]//EMNLP, 2017: 1803–1807.

16. Ji G, Liu K, He S, et al. Distant supervision for relation extraction with sentence-level attention and entity descriptions[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017.

17. Wu Y, Bamman D, Russell S. Adversarial training for relation extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1778-1783.

18. Ren X, Wu Z, He W, et al. Cotype: Joint extraction of typed entities and relations with knowledge bases[C]//Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017: 1015-1024.

• 事件抽取

1. Chen Y, Xu L, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks. ACL2015, 1: 167-176.
2. Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks. ACL2015, 2: 365-371.
3. Hogenboom F, Frasincar F, Kaymak U, et al. An overview of event extraction from text. DeRiVE2011.
4. Narasimhan K, Yala A, Barzilay R. Improving information extraction by acquiring external evidence with reinforcement learning. EMNLP2016.
5. Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks. NAACL2016: 300-309.

知识融合

1. Shvaiko P, Euzenat J. Ontology matching: state of the art and future challenges. IEEE Transactions on knowledge and data engineering, 2013, 25(1): 158-176.
2. Noy N F, Musen M A. Algorithm and tool for automated ontology merging and alignment. AAAI2000.
3. Do H H, Rahm E. COMA: a system for flexible combination of schema matching approaches.VLDB2002: 610-621.
4. Doan A H, Madhavan J, Domingos P, et al. Learning to map between ontologies on the semantic web. WWW2002: 662-673.
5. Ehrig M, Staab S. QOM–quick ontology mapping. ISWC2004: 683-697.
6. Qu Y, Hu W, Cheng G. Constructing virtual documents for ontology matching. WWW2006: 23-31.
7. Li J, Tang J, Li Y, et al. RiMOM: A dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and data Engineering, 2009, 21(8): 1218-1232.
8. Mao M, Peng Y, Spring M. An adaptive ontology mapping approach with neural network based constraint satisfaction. Journal of Web Semantics, 2010, 8(1): 14-25.
9. Hu W, Qu Y, Cheng G. Matching large ontologies: A divide-and-conquer approach. Data & Knowledge Engineering, 2008, 67(1): 140-160.
10. Papadakis G, Ioannou E, Palpanas T, et al. A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2665-2682.
11. Wang P, Zhou Y, Xu B. Matching large ontologies based on reduction anchors. Twenty-Second International Joint Conference on Artificial Intelligence. 2011.
12. Niu X, Rong S, Wang H, et al. An effective rule miner for instance matching in a web of data. CIKM2012: 1085-1094.
13. Papadakis G, Ioannou E, Palpanas T, et al. A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2665-2682.
14. Li J, Wang Z, Zhang X, et al. Large scale instance matching via multiple indexes and candidate selection. Knowledge-Based Systems, 2013, 50: 112-120.
15. Hu W, Chen J, Qu Y. A self-training approach for resolving object coreference on the semantic web. WWW2011: 87-96.
16. Tang J, Fong A C M, Wang B, et al. A unified probabilistic framework for name disambiguation in digital library. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6): 975-987.
17. Zhang Y, Zhang F, Yao P, et al. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. KDD2018: 1002-1011.
18. Ngomo A C N, Auer S. LIMES—a time-efficient approach for large-scale link discovery on the web of data. IJCAI2011.
19. Sun, Zequn, Wei Hu, and Chengkai Li. Cross-lingual entity alignment via joint attribute-preserving embedding. International Semantic Web Conference. Springer, Cham, 2017.
20. Hoffart, Johannes, et al. Robust disambiguation of named entities in text. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011.

知识图谱嵌入

• ---Review---

1. Wang Q, Mao Z, Wang B, et al. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724-2743.
2. 刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展. 计算机研究与发展, 2016, 53(2): 247-261.

• ---Basic Models---

1. Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning. Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, 2010: 384-394. (one-hot)
2. Bordes A, Glorot X, Weston J, et al. Joint learning of words and meaning representations for open-text semantic parsing. Artificial Intelligence and Statistics. 2012: 127-135. (UM)
3. Bordes A, Weston J, Collobert R, et al. Learning structured embeddings of knowledge bases. AAAI. 2011. (SE)
4. Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. NIPS2013: 3111-3119.

• ---Translation-based Models(Basic Models)---

4. Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.(TransE)
5. Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.(TransH)
6. Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.(TransR/CTransR)
7. Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696. (TransD)
8. Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix. AAAI. 2016. (TansSparse)

• ---Translation-based Models(Translation Requirements Relaxing)---

9. Fan M, Zhou Q, Chang E, et al. Transition-based knowledge graph embedding with relational mapping properties. Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing. 2014. (TransM)
10. Xiao H, Huang M, Zhu X. From one point to a manifold: Knowledge graph embedding for precise link prediction. arXiv preprint arXiv:1512.04792, 2015. (ManifoldE)
11. Feng J, Huang M, Wang M, et al. Knowledge graph embedding by flexible translation. Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning. 2016. (TransF)
12. Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490, 2015. (TransA)

• ---Translation-based Models(Gaussian Distribution Models)---

13. He S, Liu K, Ji G, et al. Learning to represent knowledge graphs with gaussian embedding. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 623-632. (KB2E)
14. Xiao H, Huang M, Zhu X. TransG: A generative model for knowledge graph embedding. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2316-2325. （TransG）

• ---Semantic Matching Models(Matrix Factorization Models)---

15. Jenatton R, Roux N L, Bordes A, et al. A latent factor model for highly multi-relational data. NIPS. 2012: 3167-3175. (LFM)
16. Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. 2011, 11: 809-816. (RESCAL)
17. Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575, 2014. (DistMult)
18. Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. AAAI. 2016. (HolE)
19. Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016: 2071-2080. (ComplEx)
20. Liu H, Wu Y, Yang Y. Analogical inference for multi-relational embeddings. Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 2168-2178. (ANALOGY)

• ---Semantic Matching Models(Neural Network Models)---

21. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (SLM)
22. Bordes A, Glorot X, Weston J, et al. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2014, 94(2): 233-259. (SME)
23. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
24. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP)
25. Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models. arXiv preprint arXiv:1603.07704, 2016. (NAM)
26. Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2d knowledge graph embeddings. AAAI. 2018. (ConvE)

• ---Multi-source Information Fusion Models(Entity Type)---

27. Guo S, Wang Q, Wang B, et al. Semantically smooth knowledge graph embedding. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015, 1: 84-94. (SSE)
28. Xie R, Liu Z, Sun M. Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI. 2016: 2965-2971. (TKRL)

• ---Multi-source Information Fusion Models(Relation Paths)---

29. Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379, 2015. (PTransE)
30. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP+PRA)
31. Nickel M, Jiang X, Tresp V. Reducing the rank in relational factorization models by including observable patterns. NIPS. 2014: 1179-1187. (PRA+RESCAL)

• ---Multi-source Information Fusion Models(Textual Descriptions)---

32. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
33. Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions. AAAI. 2016. (DKRL)
34. Xiao H, Huang M, Meng L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI. 2017. (SSP)
35. Wang Z, Li J Z. Text-Enhanced Representation Learning for Knowledge Graph. IJCAI. 2016: 1293-1299. (TEKE)
36. Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP. 2014: 1591-1601.

• ---Multi-source Information Fusion Models(Logical Rules)---

37. Wang Q, Wang B, Guo L. Knowledge base completion using embeddings and rules. IJCAI. 2015.
38. Guo S, Wang Q, Wang L, et al. Jointly embedding knowledge graphs and logical rules. EMNLP. 2016: 192-202. (KALE)
39. Guo S, Wang Q, Wang L, et al. Knowledge graph embedding with iterative guidance from soft rules. AAAI. 2018. (RUGE)
40. Ding B, Wang Q, Wang B, et al. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, 2018.

• ---Multi-source Information Fusion Models(Entity Attributes)---

41. Nickel M, Tresp V, Kriegel H P. Factorizing yago: scalable machine learning for linked data. Proceedings of the 21st international conference on World Wide Web. ACM, 2012: 271-280.

• ---Multi-source Information Fusion Models(Temporal Information)---

42. Jiang T, Liu T, Ge T, et al. Encoding temporal information for time-aware link prediction. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2350-2354.

• ---Multi-source Information Fusion Models(Graph Structure)---

43. Feng J, Huang M, Yang Y. GAKE: graph aware knowledge embedding. COLING. 2016: 641-651. (GAKE)

知识推理/知识挖掘

1. Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML2011: 809-816.
2. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS2013: 926-934.
3. Lao N, Cohen W W. Relational retrieval using a combination of path-constrained random walks. Machine learning, 2010, 81(1): 53-67.
4. Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. EMNLP2015.
5. Gardner M, Talukdar P, Krishnamurthy J, et al. Incorporating vector space similarity in random walk inference over knowledge bases. EMNLP2014: 397-406.
6. Xiong W, Hoang T, Wang W Y. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. EMNLP2017:564-573.
7. Socher R , Chen D , Manning C D , et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2013.
8. Shi B , Weninger T . ProjE: Embedding Projection for Knowledge Graph Completion[J]. 2016.
9. Shi B , Weninger T . Open-World Knowledge Graph Completion[J]. 2017.
10. Schlichtkrull M , Kipf T N , Bloem P , et al. Modeling Relational Data with Graph Convolutional Networks[J]. 2017.
11. PTransE: Sun M , Zhu H , Xie R , et al. Iterative Entity Alignment via Joint Knowledge Embeddings[C]// International Joint Conference on Artificial Intelligence. AAAI Press, 2017.
12. Das R , Neelakantan A , Belanger D , et al. Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks[J]. 2016.
13. Shen Y , Huang P S , Chang M W , et al. Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion[J]. 2016.
14. Graves A , Wayne G , Reynolds M , et al. Hybrid computing using a neural network with dynamic external memory[J]. Nature.
15. Yang F , Yang Z , Cohen W W . Differentiable Learning of Logical Rules for Knowledge Base Reasoning[J]. 2017.

实体链接

1. Zhang W, Su J, Tan C L, et al. Entity linking leveraging: automatically generated annotation[C]// Proceedings of the 23rd International Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2010: 1290-1298.
2. Anastácio I, Martins B, Calado P. Supervised learning for linking named entities to knowledge base entries[C]// Proceedings of TAC. Gaithersburg: NIST, 2011: 1-12.
3. Francis-Landau M, Durrett G, Klein D. Capturing semantic similarity for entity linking with convolutional neural networks[C] Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 1256-1261.
4. Sun Y, Lin L, Tang D, et al. Modeling mention, context and entity with neural networks for entity disambiguation// Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. California: IJCAI, 2015: 1333-1339.
5. Han X, Sun L, Zhao J. Collective entity linking in web text: a graph-based method[C]// Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2011: 765-774.
6. Rao D, McNamee P, Dredze M. Entity linking: Finding extracted entities in a knowledge base// Multi-source, Multilingual Information Extraction and Summarization. Berlin: Springer, 2013:93-115.
7. Guo Z, Barbosa D. Robust entity linking via random walks[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York:ACM Press, 2014: 499-508.

知识存储/知识查询

1. Bornea M A, Dolby J, Kementsietsidis A, et al. Building an efficient RDF store over a relational database. SIGMOD2013: 121-132.
2. Huang J, Abadi D J, Ren K. Scalable SPARQL querying of large RDF graphs. Proceedings of the VLDB Endowment, 2011, 4(11): 1123-1134.
3. Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23(4): 565-590.
4. Wilkinson K, Sayers C, Kuno H, et al. Efficient RDF storage and retrieval in Jena2[C]//Proceedings of the First International Conference on Semantic Web and Databases. CEUR-WS. org, 2003: 120-139.
5. Zou L, Mo J, Chen L, et al. gStore: answering SPARQL queries via subgraph matching[J]. Proceedings of the VLDB Endowment, 2011, 4(8): 482-493.
6. Das S, Agrawal D, El Abbadi A. G-store: a scalable data store for transactional multi key access in the cloud[C]//Proceedings of the 1st ACM symposium on Cloud computing. ACM, 2010: 163-174.
7. Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23(4): 565-590.
8. Ma L, Su Z, Pan Y, et al. RStar: an RDF storage and query system for enterprise resource management[C]//Proceedings of the thirteenth ACM international conference on Information and knowledge management. ACM, 2004: 484-491.
9. Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[C]//Proceedings of the VLDB Endowment. VLDB Endowment, 2013, 6(4): 265-276.
10. Sakr S, Al-Naymat G. Relational processing of RDF queries: a survey[J]. ACM SIGMOD Record, 2010, 38(4): 23-28.
11. Harris S, Shadbolt N. SPARQL query processing with conventional relational database systems[C]//International Conference on Web Information Systems Engineering. Springer, Berlin, Heidelberg, 2005: 235-244.
12. Angles R. A comparison of current graph database models[C]//2012 IEEE 28th International Conference on Data Engineering Workshops. IEEE, 2012: 171-177.
13. Miller J J. Graph database applications and concepts with Neo4j[C]//Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA. 2013, 2324(S 36).
14. Iordanov B. HyperGraphDB: a generalized graph database[C]//International conference on web-age information management. Springer, Berlin, Heidelberg, 2010: 25-36.
15. Sun J, Jin Q. Scalable rdf store based on hbase and mapreduce[C]//2010 3rd international conference on advanced computer theory and engineering (ICACTE). IEEE, 2010, 1: V1-633-V1-636.
16. Huang J, Abadi D J, Ren K. Scalable SPARQL querying of large RDF graphs[J]. Proceedings of the VLDB Endowment, 2011, 4(11): 1123-1134.
17. Weiss C, Karras P, Bernstein A. Hexastore: sextuple indexing for semantic web data management[J]. Proceedings of the VLDB Endowment, 2008, 1(1): 1008-1019.
18. Neumann T, Weikum G. The RDF-3X engine for scalable management of RDF data[J]. The VLDB Journal—The International Journal on Very Large Data Bases, 2010, 19(1): 91-113.

人机交互

1. Ferrucci D A. Introduction to “this is watson”. IBM Journal of Research and Development, 2012, 56(3.4): 1:1-1:15.
2. Lally A, Prager J M, McCord M C, et al. Question analysis: How Watson reads a clue. IBM Journal of Research and Development, 2012, 56(3.4): 2: 1-2: 14.
3. Zhou H, Young T, Huang M, et al. Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI. 2018: 4623-4629.
4. Zhu Y, Zhang C, Ré C, et al. Building a large-scale multimodal knowledge base system for answering visual queries. arXiv:1507.05670, 2015.
5. Auli M, Galley M, Quirk C, et al. Joint language and translation modeling with recurrent neural networks. EMNLP2013:1044–1054.
6. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
7. Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP2014.
8. Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
9. Graves A. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

附录B：最新进展论文选读(近1年内)

1. Bhatia S, Dwivedi P, Kaur A. That’s Interesting, Tell Me More! Finding Descriptive Support Passages for Knowledge Graph Relationships. ISWC2018: 250-267. (Best Paper)
2. Soulet A, Giacometti A, Markhoff B, et al. Representativeness of Knowledge Bases with the Generalized Benford’s Law. ISWC2018: 374-390.
3. Wang M, Wang R, Liu J, et al. Towards Empty Answers in SPARQL: Approximating Querying with RDF Embedding. ISWC2018: 513-529.
4. Salas J, Hogan A. Canonicalisation of monotone SPARQL queries. ISWC2018: 600-616. (Best Student Paper)
5. Pertsas V, Constantopoulos P, Androutsopoulos I. Ontology Driven Extraction of Research Processes. ISWC2018:162-178.
6. Saeedi A, Peukert E, Rahm E. Using link features for entity clustering in knowledge graphs. ESWC2018: 576-592. (Best Paper)
7. Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks. ESWC2018: 593-607. (Best Student Paper)
8. Hamid Z, Giulio N, Jens L. Formal Query Generation for Question Answering over Knowledge Bases. ESWC2018:714-728.
9. Zhou L, Gao J, Li D, et al. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. arXiv preprint arXiv:1812.08989, 2018.
10. Dasgupta S S, Ray S N, Talukdar P. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP2018: 2001-2011.
11. Dubey M, Banerjee D, Chaudhuri D, et al. EARL: Joint entity and relation linking for question answering over knowledge graphsISWC2018: 108-126.
12. Chen M, Tian Y, Chang K W, et al. Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. IJCAI2018.
13. Janke D, Staab S, Thimm M. Impact analysis of data placement strategies on query efforts in distributed rdf stores. Journal of Web Semantics, 2018, 50: 21-48.
14. Han X, Zhu H, Yu P, et al. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation. EMNLP2018.
15. Hou Y, Liu Y, Che W, et al. Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding. ACL2018: 1234-1245.
16. Tran V K, Nguyen L M. Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems. COLING2018: 1205-1217.
17. Zhang W, Cui Y, Wang Y, et al. Context-Sensitive Generation of Open-Domain Conversational Responses. COLING2018: 2437-2447.
18. Shi W, Yu Z. Sentiment Adaptive End-to-End Dialog Systems. ACL2018, 1: 1509-1519.
19. Zhang S, Dinan E, Urbanek J, et al. Personalizing Dialogue Agents: I have a dog, do you have pets too? ACL2018, 1: 2204-2213.
20. Wei Z, Liu Q, Peng B, et al. Task-oriented dialogue system for automatic diagnosis. ACL2018, 2: 201-207.
21. Sungjoon Park, Donghyun Kim and Alice Oh. Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues. NAACL2019.
22. Sebastian R. Neural Transfer Learning for Natural Language Processing. PhD Thesis. National University of Ireland, 2019.
23. Cao, Yixin, et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. The World Wide Web Conference. ACM, 2019. code
24. Rajarshi Bhowmik, Gerard de Melo. [Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts.] (https://arxiv.org/pdf/1904.07391) The World Wide Web Conference. ACM, 2019.

• ---实体识别(ACL)---

23. Parvez M R, Chakraborty S, Ray B, et al. Building language models for text with named entities. arXiv preprint arXiv:1805.04836, 2018.
24. Lin Y, Yang S, Stoyanov V, et al. A multi-lingual multi-task architecture for low-resource sequence labeling. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 799-809.
25. Xu H, Liu B, Shu L, et al. Double embeddings and cnn-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601, 2018.
26. Ye Z X, Ling Z H. Hybrid semi-markov crf for neural sequence labeling. arXiv preprint arXiv:1805.03838, 2018.
27. Yang J, Zhang Y. Ncrf++: An open-source neural sequence labeling toolkit. arXiv preprint arXiv:1806.05626, 2018.

• ---实体识别(NAACL)---

28. Ju M, Miwa M, Ananiadou S. A neural layered model for nested named entity recognition. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018, 1: 1446-1459.
29. Wang Z, Qu Y, Chen L, et al. Label-aware double transfer learning for cross-specialty medical named entity recognition. NAACL2018.
30. Moon S, Neves L, Carvalho V. Multimodal named entity recognition for short social ../media posts. NAACL2018.
31. Katiyar A, Cardie C. Nested named entity recognition revisited. NAACL2018: 861-871.

• ---实体识别(EMNLP)---

32. Cao P, Chen Y, Liu K, et al. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism.EMNLP2018: 182-192.
33. Xie J, Yang Z, Neubig G, et al. Neural cross-lingual named entity recognition with minimal resources. EMNLP2018.
34. Lin B Y, Lu W. Neural adaptation layers for cross-domain named entity recognition. EMNLP2018.
35. Shang J, Liu L, Ren X, et al. Learning Named Entity Tagger using Domain-Specific Dictionary. EMNLP2018.
36. Greenberg N, Bansal T, Verga P, et al. Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets. EMNLP2018: 2824-2829.
37. Sohrab M G, Miwa M. Deep Exhaustive Model for Nested Named Entity Recognition.EMNLP2018: 2843-2849.
38. Yu X, Mayhew S, Sammons M, et al. On the Strength of Character Language Models for Multilingual Named Entity Recognition. EMNLP2018.

• ---实体识别(COLING)---

39. Mai K, Pham T H, Nguyen M T, et al. An empirical study on fine-grained named entity recognition. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 711-722.
40. Nagesh A, Surdeanu M. An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2312-2324.
41. Bhutani N, Qian K, Li Y, et al. Exploiting Structure in Representation of Named Entities using Active Learning. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 687-699.
42. Yadav V, Bethard S. A survey on recent advances in named entity recognition from deep learning models. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2145-2158.
43. Güngör O, Üsküdarlı S, Güngör T. Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags. arXiv preprint arXiv:1807.06683, 2018.
44. Chen L, Moschitti A. Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2181-2191.
45. Ghaddar A, Langlais P. Robust lexical features for improved neural network named-entity recognition. COLING2018.

• ---事件抽取(ACL)---

46. Choubey P K, Huang R. Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures.Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 485-495.
47. Lin H, Lu Y, Han X, et al. Nugget Proposal Networks for Chinese Event Detection. ACL2018.
48. Huang L, Ji H, Cho K, et al. Zero-shot transfer learning for event extraction. ACL2017.
49. Hong Y, Zhou W, Zhang J, et al. Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 515-526.
50. Zhao Y, Jin X, Wang Y, et al. Document embedding enhanced event detection with hierarchical and supervised attention. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018, 2: 414-419.
51. Yang H, Chen Y, Liu K, et al. DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data. ACL2018, System Demonstrations, 2018: 50-55.

• ---事件抽取(NAACL)---

52. Ferguson J, Lockard C, Weld D S, et al. Semi-Supervised Event Extraction with Paraphrase Clusters. ACL2018.

• ---事件抽取(EMNLP)---

53. Orr J W, Tadepalli P, Fern X. Event Detection with Neural Networks: A Rigorous Empirical Evaluation. EMNLP2018.
54. Liu S, Cheng R, Yu X, et al. Exploiting Contextual Information via Dynamic Memory Network for Event Detection. EMNLP2018.
55. Liu X, Luo Z, Huang H. Jointly multiple events extraction via attention-based graph information aggregation. EMNLP2018.
56. Chen Y, Yang H, Liu K, et al. Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms. EMNLP2018: 1267-1276.
57. Lu W, Nguyen T H. Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4822-4828.

• ---事件抽取(COLING)---

58. Araki J, Mitamura T. Open-Domain Event Detection using Distant Supervision. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 878-891.
59. Muis A O, Otani N, Vyas N, et al. Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 70-82.
60. Kazeminejad G, Bonial C, Brown S W, et al. Automatically Extracting Qualia Relations for the Rich Event Ontology. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2644-2652.
61. Liu Z, Mitamura T, Hovy E. Graph-Based Decoding for Event Sequencing and Coreference Resolution. COLING2018.

• ---关系抽取---

62. Su Y, Liu H, Yavuz S, et al. Global relation embedding for relation extraction, NAACL2018:820-830.
63. Zeng X, He S, Liu K, et al. Large scaled relation extraction with reinforcement learning, AAAI2018.
64. Liu T, Zhang X, Zhou W, et al. Neural relation extraction via inner-sentence noise reduction and transfer learning, EMNLP2018:2195-2204.
65. Wang S, Zhang Y, Che W, et al. Joint Extraction of Entities and Relations Based on a Novel Graph Scheme, IJCAI2018: 4461-4467.
66. Feng J, Huang M, Zhao L, et al. Reinforcement learning for relation classification from noisy data, AAAI2018.
67. He Z, Chen W, Li Z, et al. SEE: Syntax-aware entity embedding for neural relation extraction, AAAI2018.
68. Vashishth S , Joshi R , Prayaga S S , et al. RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information. ACL2018.
69. Tan Z, Zhao X, Wang W, et al. Jointly Extracting Multiple Triplets with Multilayer Translation Constraints. AAAI2018.
70. Ryuichi Takanobu, Tianyang Zhang, JieXi Liu, Minlie HuangA Hierarchical Framework for Relation Extraction with Reinforcement Learning, AAAI2019.

• ---知识存储---

71. Davoudian A, Chen L, Liu M. A survey on NoSQL stores[J]. ACM Computing Surveys (CSUR), 2018, 51(2): 40.
72. Wylot M, Hauswirth M, Cudré-Mauroux P, et al. RDF data storage and query processing schemes: A survey[J]. ACM Computing Surveys (CSUR), 2018, 51(4): 84.
73. Zeng L, Zou L. Redesign of the gStore system[J]. Frontiers of Computer Science, 2018, 12(4): 623-641.
74. Zhang X, Zhang M, Peng P, et al. A Scalable Sparse Matrix-Based Join for SPARQL Query Processing[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2019: 510-514.
75. Libkin L, Reutter J L, Soto A, et al. TriAL: A navigational algebra for RDF triplestores[J]. ACM Transactions on Database Systems (TODS), 2018, 43(1): 5.
76. Elzein N M, Majid M A, Hashem I A T, et al. Managing big RDF data in clouds: Challenges, opportunities, and solutions[J]. Sustainable Cities and Society, 2018, 39: 375-386.

• ---知识推理---

77. Lin, Xi Victoria, Richard Socher, and Caiming Xiong. Multi-hop knowledge graph reasoning with reward shaping. arXiv preprint arXiv:1808.10568 (2018).
78. Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April). Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
79. Gu, L., Xia, Y., Yuan, X., Wang, C., & Jiao, J. (2018). Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph. Filomat, 32(5).
80. Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April).Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
81. Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017, August). Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 3462-3471). JMLR. org.
82. Hamilton, W., Bajaj, P., Zitnik, M., Jurafsky, D., & Leskovec, J. (2018).Embedding logical queries on knowledge graphs. In Advances in Neural Information Processing Systems (pp. 2026-2037).

• ---实体链接---

83. Sil, A., Kundu, G., Florian, R., & Hamza, W. (2018, April). Neural cross-lingual entity linking. In Thirty-Second AAAI Conference on Artificial Intelligence.
84. Chen, H., Wei, B., Liu, Y., Li, Y., Yu, J., & Zhu, W. (2018). Bilinear joint learning of word and entity embeddings for Entity Linking. Neurocomputing, 294, 12-18.
85. Raiman, J. R., & Raiman, O. M. (2018, April). DeepType: multilingual entity linking by neural type system evolution. In Thirty-Second AAAI Conference on Artificial Intelligence.
86. Kundu, G., Sil, A., Florian, R., & Hamza, W. (2018). Neural cross-lingual coreference resolution and its application to entity linking. arXiv preprint arXiv:1806.10201.
87. Kilias, T., Löser, A., Gers, F. A., Koopmanschap, R., Zhang, Y., & Kersten, M. (2018). Idel: In-database entity linking with neural embeddings. arXiv preprint arXiv:1803.04884.
88. Cao, Y., Hou, L., Li, J., & Liu, Z. (2018). Neural collective entity linking. arXiv preprint arXiv:1811.08603.

• ---知识融合---
  Wang, Zhichun, et al. Cross-lingual knowledge graph alignment via graph convolutional networks. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

• ---知识补全---
  Xiaolan Wang, Xin Luna Dong, Yang Li, Alexandra Meliou. MIDAS: Finding the right web sources to fill knowledge gaps. ICDE2019.

• ---知识构建---
  Nguyen D B, Abujabal A, Tran N K, et al. Query-driven on-the-fly knowledge base construction. VLDB2017.

• ---知识评估---
  Gao J, Li X, Xu Y E, et al. Efficient knowledge graph accuracy evaluation. VLDB2019, 12(11): 1679-1691.

• ---知识图谱：综述、概念和发展---
  Claudio Gutierrez and Juan F. Sequeda. A Brief History of Knowledge Graph's Main Ideas: A tutorial. ISWC2019 tutorial
  Noy N, Gao Y, Jain A, et al. Industry-scale knowledge graphs: Lessons and challenges. ACM Queue, 2019, 17(2): 48-75.

附录B：其它资源

Top-level Conference Publications on Knowledge Graph (2018-2020)
Stanford Spring 2021 《Knowledge Graphs》
Stanford Spring 2020 《Knowledge Graphs》