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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 （2024-02-22）

课件下载:2024-pub-0课程介绍.pdf

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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

1.2 知识图谱的技术体系剖析

1.3 知识图谱的瓶颈、问题和挑战的思考

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

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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

课件下载:2024-pub-2知识表示.pdf

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第3讲 知识建模 （2024-03-21）

3.1 本体
3.2 知识建模方法

课件下载:2024-pub-3知识建模.pdf

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第4讲 知识抽取基础:问题和方法（2024-03-28）

4.1 问题分析

4.2 知识抽取场景和方法

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

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

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

10.2 KG for LLM

10.3 KG vs LLM

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

11.2 ChatGPT在质量评测和Prompt工程方面的应用

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

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

  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.
  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.

  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.

  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

  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.
  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.
  1. Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.(TransE)
  2. Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.(TransH)
  3. Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.(TransR/CTransR)
  4. Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696. (TransD)
  5. Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix. AAAI. 2016. (TansSparse)
  1. 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)
  2. 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)
  3. 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)
  4. Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490, 2015. (TransA)
  1. 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)
  2. 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）
  1. Jenatton R, Roux N L, Bordes A, et al. A latent factor model for highly multi-relational data. NIPS. 2012: 3167-3175. (LFM)
  2. Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. 2011, 11: 809-816. (RESCAL)
  3. 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)
  4. Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. AAAI. 2016. (HolE)
  5. Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016: 2071-2080. (ComplEx)
  6. 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)
  1. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (SLM)
  2. 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)
  3. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
  4. 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)
  5. Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models. arXiv preprint arXiv:1603.07704, 2016. (NAM)
  6. Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2d knowledge graph embeddings. AAAI. 2018. (ConvE)
  1. 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)
  2. Xie R, Liu Z, Sun M. Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI. 2016: 2965-2971. (TKRL)
  1. Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379, 2015. (PTransE)
  2. 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)
  3. Nickel M, Jiang X, Tresp V. Reducing the rank in relational factorization models by including observable patterns. NIPS. 2014: 1179-1187. (PRA+RESCAL)
  1. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
  2. Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions. AAAI. 2016. (DKRL)
  3. Xiao H, Huang M, Meng L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI. 2017. (SSP)
  4. Wang Z, Li J Z. Text-Enhanced Representation Learning for Knowledge Graph. IJCAI. 2016: 1293-1299. (TEKE)
  5. Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP. 2014: 1591-1601.
  1. Wang Q, Wang B, Guo L. Knowledge base completion using embeddings and rules. IJCAI. 2015.
  2. Guo S, Wang Q, Wang L, et al. Jointly embedding knowledge graphs and logical rules. EMNLP. 2016: 192-202. (KALE)
  3. Guo S, Wang Q, Wang L, et al. Knowledge graph embedding with iterative guidance from soft rules. AAAI. 2018. (RUGE)
  4. Ding B, Wang Q, Wang B, et al. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, 2018.
  1. 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.
  1. 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.
  1. 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.
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实体链接

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知识存储/知识查询

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人机交互

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附录B：最新进展论文选读(近1年内)

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  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.
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  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.
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  5. Yang J, Zhang Y. Ncrf++: An open-source neural sequence labeling toolkit. arXiv preprint arXiv:1806.05626, 2018.
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  1. 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.
  2. 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.
  3. 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.
  4. 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.
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  6. 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.
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  1. 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.
  2. Lin H, Lu Y, Han X, et al. Nugget Proposal Networks for Chinese Event Detection. ACL2018.
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  6. 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.
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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》