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

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 partB partC

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

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

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

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

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

• 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.
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• 实体识别

• 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. 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.

• 事件抽取

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.
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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.
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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. Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.
3. Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.
4. Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.
5. Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP2014: 1591-1601.
6. Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696.

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.

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.

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.

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.