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

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 partB partC

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

第3讲 知识建模 （2019-3-15，2019-3-22）

3.1 本体
3.2 知识建模方法学

• 本体工程
• 本体学习
• 知识建模工具
• 知识建模实践

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

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.

知识抽取

• 信息抽取

1. Etzioni O, Cafarella M, Downey D, et al. Web-scale information extraction in knowitall:(preliminary results).WWW2004: 100-110.
2. Banko M, Cafarella M J, Soderland S, et al. Open information extraction from the web. IJCAI2007, 7: 2670-2676.
3. Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. EMNLP2011: 1535-1545.
4. 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.

• 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.

• 事件抽取

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.

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. 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.

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.

知识存储/知识查询

1. Bornea M A, Dolby J, Kementsietsidis A, et al. Building an efficient RDF store over a relational database. SIGMOD2013: 121-132.
2. Huang J, Abadi D J, Ren K. Scalable SPARQL querying of large RDF graphs. Proceedings of the VLDB Endowment, 2011, 4(11): 1123-1134.
3. Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23(4): 565-590.

人机交互

1. Ferrucci D A. Introduction to “this is watson”. IBM Journal of Research and Development, 2012, 56(3.4): 1: 1-1: 15.
2. Lally A, Prager J M, McCord M C, et al. Question analysis: How Watson reads a clue. IBM Journal of Research and Development, 2012, 56(3.4): 2: 1-2: 14.
3. Zhou H, Young T, Huang M, et al. Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI. 2018: 4623-4629.

附录B：最新进展论文选读(近1年内)

1. Bhatia S, Dwivedi P, Kaur A. That’s Interesting, Tell Me More! Finding Descriptive Support Passages for Knowledge Graph Relationships. ISWC2018: 250-267. (Best Paper)
2. Soulet A, Giacometti A, Markhoff B, et al. Representativeness of Knowledge Bases with the Generalized Benford’s Law. ISWC2018: 374-390.
3. Wang M, Wang R, Liu J, et al. Towards Empty Answers in SPARQL: Approximating Querying with RDF Embedding. ISWC2018: 513-529.
4. Salas J, Hogan A. Canonicalisation of monotone SPARQL queries. ISWC2018: 600-616. (Best Student Paper)
5. Pertsas V, Constantopoulos P, Androutsopoulos I. Ontology Driven Extraction of Research Processes. ISWC2018:162-178.
6. Saeedi A, Peukert E, Rahm E. Using link features for entity clustering in knowledge graphs. ESWC2018: 576-592. (Best Paper)
7. Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks. ESWC2018: 593-607. (Best Student Paper)
8. Hamid Z, Giulio N, Jens L. Formal Query Generation for Question Answering over Knowledge Bases. ESWC2018:714-728.
9. Zhou L, Gao J, Li D, et al. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. arXiv preprint arXiv:1812.08989, 2018.
10. Dasgupta S S, Ray S N, Talukdar P. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP2018: 2001-2011.
11. Dubey M, Banerjee D, Chaudhuri D, et al. EARL: Joint entity and relation linking for question answering over knowledge graphsISWC2018: 108-126.
12. Chen M, Tian Y, Chang K W, et al. Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. IJCAI2018.