# KnowledgeGraphCourse

**Repository Path**: yanhouzhen/KnowledgeGraphCourse

## Basic Information

- **Project Name**: KnowledgeGraphCourse
- **Description**: 东南大学《知识图谱》研究生课程
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 2
- **Created**: 2019-09-19
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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：2019年春季（2月下旬\~5月中旬）  
每周五下午2:00\~4:30  
地点：东南大学九龙湖校区, 纪忠楼Y205  
答疑/讨论/建议：请致信 pwang AT seu.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**:[partA](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1知识图谱概论A.pdf)   [partB](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1知识图谱概论B.pdf)   [partC](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-1知识图谱概论C.pdf)
## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-2知识表示.pdf)

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-3知识建模.pdf)

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-4知识抽取-问题和方法.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-5知识抽取-数据获取.pdf)    

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-6知识抽取-实体识别.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-7知识抽取-关系抽取.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-8知识抽取-事件抽取.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-9知识融合.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-10知识图谱表示学习.pdf)  

## 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  
11.3 RDF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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-11知识存储.pdf)  

## 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  
12.7 IBM Watson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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-12知识问答-微软小冰.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-13实体链接.pdf)  

## 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](https://github.com/npubird/KnowledgeGraphCourse/blob/master/pub-14知识推理.pdf)  

# 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](https://ai.google/research/pubs/pub45634.pdf). KDD2014: 601-610.  
1. Suchanek F M, Kasneci G, Weikum G. [Yago: a core of semantic knowledge](http://www2007.wwwconference.org/papers/paper391.pdf). WWW2007: 697-706.  
1. Hoffart J, Suchanek F M, Berberich K, et al. [YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia](https://people.mpi-inf.mpg.de/~kberberi/publications/2013-ai.pdf). Artificial Intelligence, 2013, 194: 28-61.  
1. Navigli R, Ponzetto S P. [BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network](http://web.informatik.uni-mannheim.de/ponzetto/pubs/navigli12b.pdf). Artificial Intelligence, 2012, 193: 217-250.  
1. Auer S, Bizer C, Kobilarov G, et al. [Dbpedia: A nucleus for a web of open data](http://editthis.info/images/swim/d/d8/Dbpedia_-_open_data.pdf). ISWC2007: 722-735.  
1. Mitchell T, Cohen W, Hruschka E, et al. [Never-ending learning](https://dl.acm.org/ft_gateway.cfm?id=3191513&type=pdf). Communications of the ACM, 2018, 61(5): 103-115. [earlier work](https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1879/2201)  

## 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](http://ftp.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness.pdf). [another version](http://www.corais.org/sites/default/files/ontology_development_101_aguide_to_creating_your_first_ontology.pdf) 

## 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)](http://www2004.org/proceedings/docs/1p100.pdf).WWW2004: 100-110.  
2. Banko M, Cafarella M J, Soderland S, et al. [Open information extraction from the web](https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf). IJCAI2007, 7: 2670-2676.  
3. Sarawagi S. [Information extraction](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.442.2007&rep=rep1&type=pdf). Foundations and Trends® in Databases, 2008, 1(3): 261-377.  
3. Fader A, Soderland S, Etzioni O. [Identifying relations for open information extraction](https://aclanthology.info/pdf/D/D11/D11-1142.pdf). EMNLP2011: 1535-1545.  
4. Fan J, Kalyanpur A, Gondek D C, et al. [Automatic knowledge extraction from documents](http://brenocon.com/watson_special_issue/05%20automatic%20knowledge%20extration.pdf). IBM Journal of Research and Development, 2012, 56(3.4): 5: 1-5: 10.  
5. Hearst M A. [Automatic acquisition of hyponyms from large text corpora](http://www.aclweb.org/anthology/C92-2082). ACL1992: 539-545.  

* **实体识别** 
1. Nadeau D, Sekine S. [A survey of named entity recognition and classification](https://www.jbe-platform.com/content/journals/10.1075/li.30.1.03nad). Lingvisticae Investigationes, 2007, 30(1): 3-26.  
2. Lample G, Ballesteros M, Subramanian S, et al. [Neural architectures for named entity recognition](https://arxiv.org/pdf/1603.01360.pdf). NAACL-HLT 2016.  
3. Huang Z, Xu W, Yu K. [Bidirectional LSTM-CRF models for sequence tagging](https://arxiv.org/pdf/1508.01991.pdf). arXiv preprint arXiv:1508.01991, 2015.  
4. Alhelbawy A, Gaizauskas R. [Graph ranking for collective named entity disambiguation](http://www.aclweb.org/anthology/P14-2013). ACL2014, 2: 75-80.  
5. Florian R, Ittycheriah A, Jing H, et al. [Named entity recognition through classifier combination](http://www.aclweb.org/anthology/W03-0425). HLT-NAACL2003: 168-171.  
6. Chiu J P C, Nichols E. [Named entity recognition with bidirectional LSTM-CNNs](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00104). 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](https://www.sciencedirect.com/science/article/pii/S0004370212000276). Artificial Intelligence, 2013, 194: 151-175.  
8. Santos C N, Guimaraes V. [Boosting named entity recognition with neural character embeddings](https://arxiv.org/pdf/1505.05008). 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](http://www.aclweb.org/anthology/D10-1098). EMNLP2010: 1002-1012.  
10. Shaalan K. [A survey of arabic named entity recognition and classification](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00178). Computational Linguistics, 2014, 40(2): 469-510.  
11. Speck R, Ngomo A C N. [Ensemble learning for named entity recognition](https://svn.aksw.org/papers/2014/ISWC_EL4NER/public.pdf). ISWC2014:519-534.  
12. Habibi M, Weber L, Neves M, et al. [Deep learning with word embeddings improves biomedical named entity recognition](https://academic.oup.com/bioinformatics/article/33/14/i37/3953940). 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](http://brenocon.com/watson_special_issue/09%20relation%20extraction%20and%20scoring.pdf). 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](https://www.aclweb.org/anthology/D12-1110)[C]//EMNLP, 2012: 1201-1211.

3. Liu C Y, Sun W B, Chao W H, et al. [Convolution neural network for relation extraction](https://link.springer.com/content/pdf/10.1007%2F978-3-642-53917-6.pdf)[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](http://ir.ia.ac.cn/bitstream/173211/4797/1/Relation%20Classification%20via%20Convolutional%20Deep%20Neural%20Network.pdf)[J]. 2014.

5. Santos, Cicero Nogueira dos, Bing Xiang, and Bowen Zhou. [“Classifying relations by ranking with convolutional neural networks.”](https://www.aclweb.org/anthology/P15-1061) In Proceedings of ACL, 2015.

6. Zeng D, Liu K, Chen Y, et al. [Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks](https://www.aclweb.org/anthology/D15-1203)[C]//Emnlp. 2015: 1753-1762.

7. Miwa M , Bansal M . [End-to-end Relation Extraction using LSTMs on Sequences and Tree Structures](https://www.aclweb.org/anthology/P16-1105)[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](http://anthology.aclweb.org/P16-2034)[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](https://www.aclweb.org/anthology/P16-1200)[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](https://www.aclweb.org/anthology/P16-1072)[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](http://eprints.bimcoordinator.co.uk/14/1/relation-classification.pdf)[J]. 2016.

12. Zhou P, Shi W, Tian J, et al. [Attention-based bidirectional long short-term memory networks for relation classification](https://www.aclweb.org/anthology/P16-2034)[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](https://www.aclweb.org/anthology/P16-1200)[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](https://www.aclweb.org/anthology/P17-1004)[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](https://www.aclweb.org/anthology/D17-1191)[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](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14491/14078)[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017.

17. Wu Y, Bamman D, Russell S. [Adversarial training for relation extraction](https://www.aclweb.org/anthology/D17-1187)[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](https://www.ijcai.org/proceedings/2018/0620.pdf)[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](http://www.aclweb.org/anthology/P15-1017). ACL2015, 1: 167-176.  
2. Nguyen T H, Grishman R. [Event detection and domain adaptation with convolutional neural networks](http://www.aclweb.org/anthology/P15-2060). ACL2015, 2: 365-371.  
3. Hogenboom F, Frasincar F, Kaymak U, et al. [An overview of event extraction from text](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.369.7040&rep=rep1&type=pdf). DeRiVE2011.  
4. Narasimhan K, Yala A, Barzilay R. [Improving information extraction by acquiring external evidence with reinforcement learning](https://arxiv.org/pdf/1603.07954.pdf). EMNLP2016.  
5. Nguyen T H, Cho K, Grishman R. [Joint event extraction via recurrent neural networks](http://www.aclweb.org/anthology/N16-1034). NAACL2016: 300-309.  

## 知识融合  
1. Shvaiko P, Euzenat J. [Ontology matching: state of the art and future challenges](https://hal.inria.fr/hal-00917910/document). 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](https://www.aaai.org/Papers/AAAI/2000/AAAI00-069.pdf). AAAI2000.  
3. Do H H, Rahm E. [COMA: a system for flexible combination of schema matching approaches](http://www.vldb.org/conf/2002/S17P03.pdf).VLDB2002: 610-621.  
4. Doan A H, Madhavan J, Domingos P, et al. [Learning to map between ontologies on the semantic web](http://secs.ceas.uc.edu/~mazlack/CS716.f2006/Semantic.Web.Ontology.Papers/Doan.02.pdf). WWW2002: 662-673.  
5. Ehrig M, Staab S. [QOM–quick ontology mapping](http://www.scs.carleton.ca/~armyunis/knowledge-managment/papers/QOM-Quick%20Ontology%20Mapping.pdf). ISWC2004: 683-697.  
6. Qu Y, Hu W, Cheng G. [Constructing virtual documents for ontology matching](https://www.researchgate.net/profile/Yuzhong_Qu/publication/221022499_Lecture_Notes_in_Computer_Science/links/5483bb9f0cf25dbd59eb0ff0/Lecture-Notes-in-Computer-Science.pdf). WWW2006: 23-31.  
7. Li J, Tang J, Li Y, et al. [RiMOM: A dynamic multistrategy ontology alignment framework](https://ieeexplore.ieee.org/abstract/document/4633358/). 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](http://gesispanel.gesis.org/preprints/index.php/ps/article/download/209/368). Journal of Web Semantics, 2010, 8(1): 14-25.  
9. Hu W, Qu Y, Cheng G. [Matching large ontologies: A divide-and-conquer approach](http://dit.unitn.it/~p2p/RelatedWork/Matching/MatchingLargeOntologies.pdf). 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](http://disi.unitn.it/~themis/publications/erframework-tr12.pdf). 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](https://www.aaai.org/ocs/index.php/IJCAI/IJCAI11/paper/download/3145/3697). 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](http://xingniu.org/pub/ruleminer_cikm12.pdf). CIKM2012: 1085-1094.  
13. Papadakis G, Ioannou E, Palpanas T, et al. [A blocking framework for entity resolution in highly heterogeneous information spaces](http://disi.unitn.it/~themis/publications/erframework-tr12.pdf). 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](http://disi.unitn.it/~p2p/RelatedWork/Matching/KBS13-Li-et-al-large-instance.pdf). 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](http://dit.unitn.it/~p2p/RelatedWork/Matching/A%20self-training%20approach_Hu_www11.pdf). WWW2011: 87-96.  
16. Tang J, Fong A C M, Wang B, et al. [A unified probabilistic framework for name disambiguation in digital library](http://keg.cs.tsinghua.edu.cn/jietang/publications/TKDE12-Tang-Name-Disambiguation.pdf). 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](http://keg.cs.tsinghua.edu.cn/jietang/publications/kdd18_yutao-AMiner-Name-Disambiguation.pdf). 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](https://www.aaai.org/ocs/index.php/IJCAI/IJCAI11/paper/viewFile/3125/3692). IJCAI2011.  

 
## 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](http://download.xuebalib.com/3at6CEQL3eBi.pdf). 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](http://crad.ict.ac.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=3099). 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](https://aclanthology.info/pdf/P/P10/P10-1040.pdf). 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](http://proceedings.mlr.press/v22/bordes12/bordes12.pdf). Artificial Intelligence and Statistics. 2012: 127-135. (**UM**)  
3. Bordes A, Weston J, Collobert R, et al. [Learning structured embeddings of knowledge bases](https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898). AAAI. 2011. (**SE**)  
4. Mikolov T, Sutskever I, Chen K, et al. [Distributed representations of words and phrases and their compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). 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](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf). NIPS2013: 2787-2795.(**TransE**)  
5. Wang Z, Zhang J, Feng J, et al. [Knowledge graph embedding by translating on hyperplanes](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546). AAAI2014.(**TransH**)    
6. Lin Y, Liu Z, Sun M, et al. [Learning entity and relation embeddings for knowledge graph completion](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523). AAAI2015.(**TransR/CTransR**)  
7. Ji G, He S, Xu L, et al. [Knowledge graph embedding via dynamic mapping matrix](http://www.aclweb.org/anthology/P15-1067). ACL2015: 687-696. (**TransD**)  
8. Ji G, Liu K, He S, et al. [Knowledge graph completion with adaptive sparse transfer matrix](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/11982/11693). 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](https://www.aclweb.org/anthology/Y14-1039). 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](https://arxiv.org/pdf/1512.04792). arXiv preprint arXiv:1512.04792, 2015. (**ManifoldE**)  
11. Feng J, Huang M, Wang M, et al.[ Knowledge graph embedding by flexible translation](https://www.aaai.org/ocs/index.php/KR/KR16/paper/viewPDFInterstitial/12887/12520). 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](https://arxiv.org/pdf/1509.05490). 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](http://ir.ia.ac.cn/bitstream/173211/11475/1/sig-alternate.pdf). 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](https://www.aclweb.org/anthology/P16-1219). 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](https://papers.nips.cc/paper/4744-a-latent-factor-model-for-highly-multi-relational-data.pdf). NIPS. 2012: 3167-3175. (**LFM**)  
16. Nickel M, Tresp V, Kriegel H P. [A Three-Way Model for Collective Learning on Multi-Relational Data](http://www.cip.ifi.lmu.de/~nickel/data/slides-icml2011.pdf). 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](https://arxiv.org/pdf/1412.6575). arXiv preprint arXiv:1412.6575, 2014. (**DistMult**)  
18. Nickel M, Rosasco L, Poggio T. [Holographic embeddings of knowledge graphs](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12484/11828). AAAI. 2016. (**HolE**)  
19. Trouillon T, Welbl J, Riedel S, et al. [Complex embeddings for simple link prediction](http://www.jmlr.org/proceedings/papers/v48/trouillon16.pdf). International Conference on Machine Learning. 2016: 2071-2080. (**ComplEx**)  
20. Liu H, Wu Y, Yang Y. [Analogical inference for multi-relational embeddings](https://arxiv.org/pdf/1705.02426). 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](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf). 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](https://link.springer.com/article/10.1007/s10994-013-5363-6). 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](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf). NIPS. 2013: 926-934. (**NTN**)  
24. Dong X, Gabrilovich E, Heitz G, et al.[ Knowledge vault: A web-scale approach to probabilistic knowledge fusion](https://ai.google/research/pubs/pub45634.pdf). 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](https://arxiv.org/pdf/1603.07704). arXiv preprint arXiv:1603.07704, 2016. (**NAM**)  
26. Dettmers T, Minervini P, Stenetorp P, et al. [Convolutional 2d knowledge graph embeddings](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17366/15884). AAAI. 2018. (**ConvE**)

* **---Multi-source Information Fusion Models(Entity Type)---**  
27. Guo S, Wang Q, Wang B, et al. [Semantically smooth knowledge graph embedding](https://www.aclweb.org/anthology/P15-1009). 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](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/IJCAI-16_type.pdf). 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](https://arxiv.org/pdf/1506.00379). 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](https://ai.google/research/pubs/pub45634.pdf). 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](http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf). 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](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf). NIPS. 2013: 926-934. (**NTN**)  
33. Xie R, Liu Z, Jia J, et al. [Representation learning of knowledge graphs with entity descriptions](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12216/12004). AAAI. 2016. (**DKRL**)  
34. Xiao H, Huang M, Meng L, et al. [SSP: semantic space projection for knowledge graph embedding with text descriptions](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPDFInterstitial/14306/14084). AAAI. 2017. (**SSP**)  
35. Wang Z, Li J Z. [Text-Enhanced Representation Learning for Knowledge Graph](http://qngw2014.bj.bcebos.com/upload/2016/04/%E7%8E%8B%E5%BF%97%E5%88%9A-Text-enhanced%20Representation%20Learning%20for%20Knowledge%20Graph.pdf). IJCAI. 2016: 1293-1299. (**TEKE**)  
36. Wang Z, Zhang J, Feng J, et al. [Knowledge graph and text jointly embedding](
https://www.aclweb.org/anthology/D14-1167). 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](https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/10798/10921). IJCAI. 2015.  
38. Guo S, Wang Q, Wang L, et al. [Jointly embedding knowledge graphs and logical rules](https://www.aclweb.org/anthology/D16-1019). EMNLP. 2016: 192-202. (**KALE**)  
39. Guo S, Wang Q, Wang L, et al. [Knowledge graph embedding with iterative guidance from soft rules](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16369/16011). AAAI. 2018. (**RUGE**)  
40. Ding B, Wang Q, Wang B, et al. [Improving knowledge graph embedding using simple constraints](https://arxiv.org/pdf/1805.02408). 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](http://www.dbs.ifi.lmu.de/~tresp/papers/p271.pdf). 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](https://www.aclweb.org/anthology/D16-1260). 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](https://aclanthology.info/pdf/C/C16/C16-1062.pdf). 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](http://www.cip.ifi.lmu.de/~nickel/data/slides-icml2011.pdf). ICML2011: 809-816.  
2. Socher R, Chen D, Manning C D, et al. [Reasoning with neural tensor networks for knowledge base completion](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf). NIPS2013: 926-934.  
3. Lao N, Cohen W W. [Relational retrieval using a combination of path-constrained random walks](https://link.springer.com/content/pdf/10.1007/s10994-010-5205-8.pdf). 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](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP082.pdf). EMNLP2015.  
5. Gardner M, Talukdar P, Krishnamurthy J, et al. [Incorporating vector space similarity in random walk inference over knowledge bases](http://www.aclweb.org/anthology/D14-1044). EMNLP2014: 397-406.  
6. Xiong W, Hoang T, Wang W Y. [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](http://www.aclweb.org/anthology/D17-1060). EMNLP2017:564-573.  
7. Socher R , Chen D , Manning C D , et al. [Reasoning With Neural Tensor Networks for Knowledge Base Completion[C]](https://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pdf)// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2013.  
8. Shi B , Weninger T . [ProjE: Embedding Projection for Knowledge Graph Completion[J]](https://arxiv.org/pdf/1611.05425.pdf). 2016.  
9. Shi B , Weninger T . [Open-World Knowledge Graph Completion[J]](https://arxiv.org/pdf/1711.03438.pdf). 2017.  
10. Schlichtkrull M , Kipf T N , Bloem P , et al. [Modeling Relational Data with Graph Convolutional Networks[J]](https://arxiv.org/pdf/1703.06103.pdf). 2017.  
11. PTransE: Sun M , Zhu H , Xie R , et al. [Iterative Entity Alignment via Joint Knowledge Embeddings[C]](http://nlp.csai.tsinghua.edu.cn/~lzy/publications/ijcai2017_entity.pdf)// 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]](https://arxiv.org/pdf/1607.01426.pdf). 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]](https://128.84.21.199/pdf/1611.04642v2.pdf). 2016.  
14. Graves A , Wayne G , Reynolds M , et al. [Hybrid computing using a neural network with dynamic external memory[J]](https://www.nature.com/articles/nature20101.pdf). Nature.  
15. Yang F , Yang Z , Cohen W W . [Differentiable Learning of Logical Rules for Knowledge Base Reasoning[J]](https://arxiv.org/pdf/1702.08367.pdf). 2017.  


## 实体链接
1. Zhang W, Su J, Tan C L, et al.[ Entity linking leveraging: automatically generated annotation[C]](https://www.aclweb.org/anthology/C10-1145)// 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]](https://tac.nist.gov//publications/2011/participant.papers/dmir_inescid.proceedings.pdf)// 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]](https://arxiv.org/pdf/1604.00734.pdf)
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](https://www.ijcai.org/Proceedings/15/Papers/192.pdf)// 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]](http://www.nlpr.ia.ac.cn/2011papers/gjhy/gh133.pdf)// 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](http://www.cs.jhu.edu/~delip/entity_linking.pdf)// Multi-source, Multilingual Information Extraction and Summarization. Berlin: Springer, 2013:93-115.  
7. Guo Z, Barbosa D. [Robust entity linking via random walks[C]](https://dl.acm.org/citation.cfm?id=2661887)//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](https://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). SIGMOD2013: 121-132.  
2. Huang J, Abadi D J, Ren K. [Scalable SPARQL querying of large RDF graphs](http://www.cs.umd.edu/~abadi/papers/sw-graph-scale.pdf). 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](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.386.7427&rep=rep1&type=pdf). 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](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.221.3451&rep=rep1&type=pdf#page=137)[C]//Proceedings of the First International Conference on Semantic Web and Databases. CEUR-WS. org, 2003: 120-139.  
2. Zou L, Mo J, Chen L, et al. [gStore: answering SPARQL queries via subgraph matching](http://www.vldb.org/pvldb/vol4/p482-zou.pdf)[J]. Proceedings of the VLDB Endowment, 2011, 4(8): 482-493.  
3. Das S, Agrawal D, El Abbadi A. [G-store: a scalable data store for transactional multi key access in the cloud](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.209.1087&rep=rep1&type=pdf)[C]//Proceedings of the 1st ACM symposium on Cloud computing. ACM, 2010: 163-174.  
4. Zou L, Özsu M T, Chen L, et al. [gStore: a graph-based SPARQL query engine](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.386.7427&rep=rep1&type=pdf)[J]. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23(4): 565-590.  
5. Ma L, Su Z, Pan Y, et al. [RStar: an RDF storage and query system for enterprise resource management](https://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)[C]//Proceedings of the thirteenth ACM international conference on Information and knowledge management. ACM, 2004: 484-491.  
6.  Zeng K, Yang J, Wang H, et al. [A distributed graph engine for web scale RDF data](https://www.graphengine.io/downloads/papers/Trinity.RDF.pdf)[C]//Proceedings of the VLDB Endowment. VLDB Endowment, 2013, 6(4): 265-276.  
7.  Sakr S, Al-Naymat G. [Relational processing of RDF queries: a survey](https://sigmodrecord.org/publications/sigmodRecord/0912/p23.survey.sakr.pdf)[J]. ACM SIGMOD Record, 2010, 38(4): 23-28.  
8.  Harris S, Shadbolt N. [SPARQL query processing with conventional relational database systems](https://eprints.soton.ac.uk/261126/1/harris-ssws05.pdf)[C]//International Conference on Web Information Systems Engineering. Springer, Berlin, Heidelberg, 2005: 235-244.  
9.  Angles R. [A comparison of current graph database models](https://www.researchgate.net/profile/Renzo_Angles/publication/261076480_A_Comparison_of_Current_Graph_Database_Models/links/54f05b180cf25f74d72609c3.pdf)[C]//2012 IEEE 28th International Conference on Data Engineering Workshops. IEEE, 2012: 171-177.  
10. Miller J J. [Graph database applications and concepts with Neo4j](https://pdfs.semanticscholar.org/322a/6e1f464330751dea2eb6beecac24466322ad.pdf)[C]//Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA. 2013, 2324(S 36).  
11. Iordanov B. [HyperGraphDB: a generalized graph database](https://www.researchgate.net/profile/Borislav_Iordanov/publication/225204980_HyperGraphDB_A_Generalized_Graph_Database/links/0fcfd509bfc6de5b9a000000/HyperGraphDB-A-Generalized-Graph-Database.pdf)[C]//International conference on web-age information management. Springer, Berlin, Heidelberg, 2010: 25-36.  
12. Sun J, Jin Q. [Scalable rdf store based on hbase and mapreduce](https://ieeexplore.ieee.org/abstract/document/5578937/)[C]//2010 3rd international conference on advanced computer theory and engineering (ICACTE). IEEE, 2010, 1: V1-633-V1-636.  
13. Huang J, Abadi D J, Ren K. [Scalable SPARQL querying of large RDF graphs](http://www.cs.umd.edu/~abadi/papers/sw-graph-scale.pdf)[J]. Proceedings of the VLDB Endowment, 2011, 4(11): 1123-1134.  
14. Weiss C, Karras P, Bernstein A. [Hexastore: sextuple indexing for semantic web data management](http://people.csail.mit.edu/tdanford/6830papers/weiss-hexastore.pdf)[J]. Proceedings of the VLDB Endowment, 2008, 1(1): 1008-1019.  
15. Neumann T, Weikum G. [The RDF-3X engine for scalable management of RDF data](https://pure.mpg.de/rest/items/item_1324253/component/file_1324252/content)[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”](https://ieeexplore.ieee.org/abstract/document/6177724/). 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](http://www.patwardhans.net/papers/LallyEtAl12.pdf). 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](https://www.ijcai.org/proceedings/2018/0643.pdf). 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](https://pdfs.semanticscholar.org/9563/d6fafb6ba09c082a57e8d9b31494029a45ac.pdf). arXiv:1507.05670, 2015.  
5. Auli M, Galley M, Quirk C, et al. [Joint language and translation modeling with recurrent neural networks](http://www.aclweb.org/anthology/D13-1106). EMNLP2013:1044–1054.  
6. Bahdanau D, Cho K, Bengio Y. [Neural machine translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473). 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](http://anthology.aclweb.org/D/D14/D14-1179.pdf). EMNLP2014.  
8. Chung J, Gulcehre C, Cho K H, et al. [Empirical evaluation of gated recurrent neural networks on sequence modeling](https://arxiv.org/abs/1412.3555). arXiv preprint arXiv:1412.3555, 2014.  
9. Graves A. [Generating sequences with recurrent neural networks](https://arxiv.org/abs/1308.0850). arXiv preprint arXiv:1308.0850, 2013.  
10. 

# 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](http://sumitbhatia.net/papers/iswc18.pdf). ISWC2018: 250-267. (Best Paper)  
2. Soulet A, Giacometti A, Markhoff B, et al. [Representativeness of Knowledge Bases with the Generalized Benford’s Law](https://a3nm.net/work/seminar/slides/20181115-soulet.pdf). ISWC2018: 374-390.  
3. Wang M, Wang R, Liu J, et al. [Towards Empty Answers in SPARQL: Approximating Querying with RDF Embedding](https://link.springer.com/chapter/10.1007/978-3-030-00671-6_30). ISWC2018: 513-529.  
4. Salas J, Hogan A. [Canonicalisation of monotone SPARQL queries](https://link.springer.com/chapter/10.1007/978-3-030-00671-6_35). ISWC2018: 600-616. (Best Student Paper)  
5. Pertsas V, Constantopoulos P, Androutsopoulos I. [Ontology Driven Extraction of Research Processes](https://pages.cs.aueb.gr/ipl/nlp/pubs/iswc2018.pdf). ISWC2018:162-178.  
6. Saeedi A, Peukert E, Rahm E. [Using link features for entity clustering in knowledge graphs](https://dbs.uni-leipzig.de/file/eswc_0.pdf). ESWC2018: 576-592. (Best Paper)  
7. Schlichtkrull M, Kipf T N, Bloem P, et al. [Modeling relational data with graph convolutional networks](https://arxiv.org/pdf/1703.06103). 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](https://arxiv.org/pdf/1812.08989). arXiv preprint arXiv:1812.08989, 2018.  
10. Dasgupta S S, Ray S N, Talukdar P. [HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding](http://www.aclweb.org/anthology/D18-1225). EMNLP2018: 2001-2011.  
11. Dubey M, Banerjee D, Chaudhuri D, et al. [EARL: Joint entity and relation linking for question answering over knowledge graphs](https://arxiv.org/pdf/1801.03825)ISWC2018: 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](http://yellowstone.cs.ucla.edu/~muhao/slides/kdcoe.pdf). IJCAI2018.  
13. Janke D, Staab S, Thimm M. [Impact analysis of data placement strategies on query efforts in distributed rdf stores](http://mail.websemanticsjournal.org/preprints/index.php/ps/article/view/516/533). 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](https://arxiv.org/pdf/1810.10147). EMNLP2018.  
15. Hou Y, Liu Y, Che W, et al. [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](http://www.aclweb.org/anthology/C18-1105). ACL2018: 1234-1245.  
16. Tran V K, Nguyen L M. [Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems](http://www.aclweb.org/anthology/C18-1103). COLING2018: 1205-1217.  
17. Zhang W, Cui Y, Wang Y, et al. [Context-Sensitive Generation of Open-Domain Conversational Responses](http://www.aclweb.org/anthology/C18-1206). COLING2018: 2437-2447.  
18. Shi W, Yu Z. [Sentiment Adaptive End-to-End Dialog Systems](http://www.aclweb.org/anthology/P18-1140). 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?](http://www.aclweb.org/anthology/P18-1205) ACL2018, 1: 2204-2213.  
20. Wei Z, Liu Q, Peng B, et al. [Task-oriented dialogue system for automatic diagnosis](http://www.aclweb.org/anthology/P18-2033). 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](http://ruder.io/thesis/neural_transfer_learning_for_nlp.pdf). PhD Thesis. National University of Ireland, 2019.

* **---实体识别(ACL)---** 
23. Parvez M R, Chakraborty S, Ray B, et al. [Building language models for text with named entities](https://arxiv.org/pdf/1805.04836.pdf). 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](http://www.aclweb.org/anthology/P18-1074). 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](https://arxiv.org/pdf/1805.04601.pdf). arXiv preprint arXiv:1805.04601, 2018.  
26. Ye Z X, Ling Z H. [Hybrid semi-markov crf for neural sequence labeling](https://arxiv.org/pdf/1805.03838.pdf). arXiv preprint arXiv:1805.03838, 2018.  
27. Yang J, Zhang Y. [Ncrf++: An open-source neural sequence labeling toolkit](https://arxiv.org/pdf/1806.05626.pdf). 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](http://www.aclweb.org/anthology/N18-1131). 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](https://arxiv.org/pdf/1804.09021.pdf). NAACL2018.  
30. Moon S, Neves L, Carvalho V. [Multimodal named entity recognition for short social ../media posts](https://arxiv.org/pdf/1802.07862.pdf). NAACL2018.  
31. Katiyar A, Cardie C. [Nested named entity recognition revisited](http://www.aclweb.org/anthology/N18-1079). 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](http://www.aclweb.org/anthology/D18-1017).EMNLP2018: 182-192.  
33. Xie J, Yang Z, Neubig G, et al. [Neural cross-lingual named entity recognition with minimal resources](https://arxiv.org/pdf/1808.09861.pdf). EMNLP2018.  
34. Lin B Y, Lu W. [Neural adaptation layers for cross-domain named entity recognition](https://arxiv.org/pdf/1810.06368.pdf). EMNLP2018.  
35. Shang J, Liu L, Ren X, et al. [Learning Named Entity Tagger using Domain-Specific Dictionary](https://arxiv.org/pdf/1809.03599.pdf). 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](http://www.aclweb.org/anthology/D18-1306). EMNLP2018: 2824-2829.  
37. Sohrab M G, Miwa M. [Deep Exhaustive Model for Nested Named Entity Recognition](http://www.aclweb.org/anthology/D18-1309).EMNLP2018: 2843-2849.  
38. Yu X, Mayhew S, Sammons M, et al. [On the Strength of Character Language Models for Multilingual Named Entity Recognition](https://arxiv.org/pdf/1809.05157.pdf). 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](http://www.aclweb.org/anthology/C18-1060). 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](http://www.aclweb.org/anthology/C18-1196). 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](http://www.aclweb.org/anthology/C18-1058). 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](http://www.aclweb.org/anthology/C18-1182). 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](https://arxiv.org/pdf/1807.06683.pdf). arXiv preprint arXiv:1807.06683, 2018.  
44. Chen L, Moschitti A. [Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models](http://www.aclweb.org/anthology/C18-1185). 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](https://arxiv.org/pdf/1806.03489.pdf). 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](http://www.aclweb.org/anthology/P18-1045).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](https://arxiv.org/pdf/1805.00249.pdf). ACL2018.  
48. Huang L, Ji H, Cho K, et al. [Zero-shot transfer learning for event extraction](https://arxiv.org/pdf/1707.01066.pdf). ACL2017.  
49. Hong Y, Zhou W, Zhang J, et al. [Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection](http://www.aclweb.org/anthology/P18-1048). 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](http://www.aclweb.org/anthology/P18-2066). 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](http://www.aclweb.org/anthology/P18-4009). 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](https://arxiv.org/pdf/1808.08622.pdf). ACL2018.  

* **---事件抽取(EMNLP)---** 
53. Orr J W, Tadepalli P, Fern X. [Event Detection with Neural Networks: A Rigorous Empirical Evaluation](https://arxiv.org/pdf/1808.08504.pdf). EMNLP2018.  
54. Liu S, Cheng R, Yu X, et al. [Exploiting Contextual Information via Dynamic Memory Network for Event Detection](https://arxiv.org/pdf/1810.03449.pdf). EMNLP2018.  
55. Liu X, Luo Z, Huang H. [Jointly multiple events extraction via attention-based graph information aggregation](https://arxiv.org/pdf/1809.09078.pdf). 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](http://www.aclweb.org/anthology/D18-1158). EMNLP2018: 1267-1276.  
57. Lu W, Nguyen T H. [Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching](http://www.aclweb.org/anthology/D18-1517). 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](http://www.aclweb.org/anthology/C18-1075). 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](http://www.aclweb.org/anthology/C18-1007). 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](http://www.aclweb.org/anthology/C18-1224). 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](https://arxiv.org/pdf/1806.05099.pdf). COLING2018.  

* **---关系抽取---** 
62. Su Y, Liu H, Yavuz S, et al. [Global relation embedding for relation extraction](https://www.aclweb.org/anthology/N18-1075), NAACL2018:820-830.  
63. Zeng X, He S, Liu K, et al. [Large scaled relation extraction with reinforcement learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16257/16125), AAAI2018.  
64. Liu T, Zhang X, Zhou W, et al. [Neural relation extraction via inner-sentence noise reduction and transfer learning](https://aclweb.org/anthology/D18-1243), EMNLP2018:2195-2204.  
65. Wang S, Zhang Y, Che W, et al. [Joint Extraction of Entities and Relations Based on a Novel Graph Scheme](http://ir.hit.edu.cn/~car/papers/ijcai18slwang.pdf), IJCAI2018: 4461-4467.  
66. Feng J, Huang M, Zhao L, et al.[ Reinforcement learning for relation classification from noisy data](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17151/16140), AAAI2018.  
67. He Z, Chen W, Li Z, et al. [SEE: Syntax-aware entity embedding for neural relation extraction](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16362/16142), AAAI2018.  
68. Vashishth S , Joshi R , Prayaga S S , et al. [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://www.aclweb.org/anthology/D18-1157). ACL2018.  
69. Tan Z, Zhao X, Wang W, et al. [Jointly Extracting Multiple Triplets with Multilayer Translation Constraints](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17151/16140). AAAI2018.  
70. Ryuichi Takanobu, Tianyang Zhang, JieXi Liu, Minlie Huang[A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://arxiv.org/pdf/1811.03925.pdf), AAAI2019.  

* **---知识存储---** 
71. Davoudian A, Chen L, Liu M. [A survey on NoSQL stores](https://dl.acm.org/citation.cfm?id=3158661)[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](https://exascale.info/assets/pdf/wylot2018survey.pdf)[J]. ACM Computing Surveys (CSUR), 2018, 51(4): 84.  
73. Zeng L, Zou L. [Redesign of the gStore system](https://link.springer.com/article/10.1007/s11704-018-7212-z)[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](https://link.springer.com/chapter/10.1007/978-3-030-18590-9_77)[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](http://www.research.ed.ac.uk/portal/files/44424184/tripalg_2.pdf)[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](https://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)[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.](https://arxiv.org/pdf/1808.10568.pdf) 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.]((https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16983/16176)) 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.](http://journal.pmf.ni.ac.rs/filomat/index.php/filomat/article/download/6806/2760) 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.](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16983/16176) 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.](https://arxiv.org/pdf/1705.05742) 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.](https://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf) 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.](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPDFInterstitial/16501/16101) 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.](https://www.sciencedirect.com/science/article/pii/S0925231217318234) Neurocomputing, 294, 12-18.  
85. Raiman, J. R., & Raiman, O. M. (2018, April). [DeepType: multilingual entity linking by neural type system evolution.](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPDFInterstitial/17148/16094) 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.](https://arxiv.org/pdf/1806.10201) 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.](https://arxiv.org/pdf/1803.04884) arXiv preprint arXiv:1803.04884.  
88. Cao, Y., Hou, L., Li, J., & Liu, Z. (2018). [Neural collective entity linking.](https://arxiv.org/pdf/1811.08603) arXiv preprint arXiv:1811.08603.