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
课件下载:pub-0课程介绍.pdf
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1.1 认知智能的知识图谱视角浅析
- 知识视角的认知智能
- 知识图谱本质
- 知识图谱的演化和形成过程
- 知识图谱 VS 深度学习
- 知识图谱 VS 传统知识库 VS 数据库
- 知识图谱应用场景
- 知识图谱本质和核心价值
1.2 知识图谱的技术体系剖析
- 知识抽取
- 知识融合
- 知识表示学习
- 知识推理
- 知识存储
1.3 知识图谱的瓶颈、问题和挑战的思考
- 知识构建挑战
- 知识质量挑战
- 知识的智能应用挑战
课件下载:partA partB
课件下载百度云链接:pub-1知识图谱-理论-技术-实践和挑战.pdf 提取码:kgkg
2.1 知识表示概念
2.2 知识表示方法
- 语义网络
- 产生式系统
- 框架系统
- 概念图
- 形式概念分析
- 描述逻辑
- 本体
- 本体语言
- 知识图谱表示学习
课件下载:pub-2知识表示.pdf
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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:pub-3知识建模.pdf
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4.1 问题分析
- 知识抽取场景
- 知识抽取挑战
4.2 知识抽取场景和方法
- 面向结构化数据的知识抽取
- 面向半结构化数据的知识抽取
- 面向无结构化数据的知识抽取
课件下载:pub-4知识抽取-问题与方法.pdf
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5.1 实体识别基本概念
5.2 基于规则和词典的实体识别方法
5.3 基于机器学习的实体识别方法
5.4 基于深度学习的实体识别方法
5.5 基于半监督学习的实体识别方法
5.6 基于迁移学习的实体识别方法
5.7 基于预训练的实体识别方法
5.8 实体识别研究前沿进展
课件下载:pub-5知识抽取-实体识别.pdf
课件下载:pub-5实体识别研究前沿进展2022.pdf
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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:pub-6知识抽取-关系抽取.pdf
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7.1 事件抽取基本概念
7.2 事件抽取方法
- 基于规则和模板的事件抽取方法
- 基于机器学习的事件抽取方法
- 基于深度学习的事件抽取方法
- 基于知识库的事件抽取方法
7.3 金融领域事件抽取系统实现
7.4 事理图谱的研究与应用
课件下载:pub-7知识抽取-事件抽取.pdf
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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:pub-8知识融合.pdf
课件下载:pub-8知识融合前沿进展.pdf
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9.1 知识表示学习原理及概念
9.2 知识表示学习方法
- 基于翻译的表示学习模型
- 基于语义匹配的表示学习模型
- 融合多源信息的表示学习模型
9.3 知识图谱表示学习模型的评测
9.4 知识图谱表示学习前沿进展和挑战
课件下载:partA partB
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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
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
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
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
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- 实体识别
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- 关系抽取
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- 事件抽取
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- Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks. ACL2015, 2: 365-371.
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- Narasimhan K, Yala A, Barzilay R. Improving information extraction by acquiring external evidence with reinforcement learning. EMNLP2016.
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- 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.
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- ---Review---
- 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.
- 刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展. 计算机研究与发展, 2016, 53(2): 247-261.
- ---Basic Models---
- 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)
- 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)
- Bordes A, Weston J, Collobert R, et al. Learning structured embeddings of knowledge bases. AAAI. 2011. (SE)
- Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. NIPS2013: 3111-3119.
- ---Translation-based Models(Basic Models)---
- Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.(TransE)
- Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.(TransH)
- Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.(TransR/CTransR)
- Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696. (TransD)
- Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix. AAAI. 2016. (TansSparse)
- ---Translation-based Models(Translation Requirements Relaxing)---
- 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)
- 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)
- 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)
- Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490, 2015. (TransA)
- ---Translation-based Models(Gaussian Distribution Models)---
- 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)
- Xiao H, Huang M, Zhu X. TransG: A generative model for knowledge graph embedding. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2316-2325. (TransG)
- ---Semantic Matching Models(Matrix Factorization Models)---
- Jenatton R, Roux N L, Bordes A, et al. A latent factor model for highly multi-relational data. NIPS. 2012: 3167-3175. (LFM)
- Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. 2011, 11: 809-816. (RESCAL)
- 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)
- Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. AAAI. 2016. (HolE)
- Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016: 2071-2080. (ComplEx)
- Liu H, Wu Y, Yang Y. Analogical inference for multi-relational embeddings. Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 2168-2178. (ANALOGY)
- ---Semantic Matching Models(Neural Network Models)---
- Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (SLM)
- 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)
- Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
- 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)
- Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models. arXiv preprint arXiv:1603.07704, 2016. (NAM)
- Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2d knowledge graph embeddings. AAAI. 2018. (ConvE)
- ---Multi-source Information Fusion Models(Entity Type)---
- 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)
- Xie R, Liu Z, Sun M. Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI. 2016: 2965-2971. (TKRL)
- ---Multi-source Information Fusion Models(Relation Paths)---
- Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379, 2015. (PTransE)
- 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)
- Nickel M, Jiang X, Tresp V. Reducing the rank in relational factorization models by including observable patterns. NIPS. 2014: 1179-1187. (PRA+RESCAL)
- ---Multi-source Information Fusion Models(Textual Descriptions)---
- Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
- Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions. AAAI. 2016. (DKRL)
- Xiao H, Huang M, Meng L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI. 2017. (SSP)
- Wang Z, Li J Z. Text-Enhanced Representation Learning for Knowledge Graph. IJCAI. 2016: 1293-1299. (TEKE)
- Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP. 2014: 1591-1601.
- ---Multi-source Information Fusion Models(Logical Rules)---
- Wang Q, Wang B, Guo L. Knowledge base completion using embeddings and rules. IJCAI. 2015.
- Guo S, Wang Q, Wang L, et al. Jointly embedding knowledge graphs and logical rules. EMNLP. 2016: 192-202. (KALE)
- Guo S, Wang Q, Wang L, et al. Knowledge graph embedding with iterative guidance from soft rules. AAAI. 2018. (RUGE)
- Ding B, Wang Q, Wang B, et al. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, 2018.
- ---Multi-source Information Fusion Models(Entity Attributes)---
- Nickel M, Tresp V, Kriegel H P. Factorizing yago: scalable machine learning for linked data. Proceedings of the 21st international conference on World Wide Web. ACM, 2012: 271-280.
- ---Multi-source Information Fusion Models(Temporal Information)---
- Jiang T, Liu T, Ge T, et al. Encoding temporal information for time-aware link prediction. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2350-2354.
- ---Multi-source Information Fusion Models(Graph Structure)---
- Feng J, Huang M, Yang Y. GAKE: graph aware knowledge embedding. COLING. 2016: 641-651. (GAKE)
- Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML2011: 809-816.
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- 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)
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- Dasgupta S S, Ray S N, Talukdar P. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP2018: 2001-2011.
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- Shi W, Yu Z. Sentiment Adaptive End-to-End Dialog Systems. ACL2018, 1: 1509-1519.
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- ---实体识别(ACL)---
- Parvez M R, Chakraborty S, Ray B, et al. Building language models for text with named entities. arXiv preprint arXiv:1805.04836, 2018.
- Lin Y, Yang S, Stoyanov V, et al. A multi-lingual multi-task architecture for low-resource sequence labeling. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 799-809.
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- Ye Z X, Ling Z H. Hybrid semi-markov crf for neural sequence labeling. arXiv preprint arXiv:1805.03838, 2018.
- Yang J, Zhang Y. Ncrf++: An open-source neural sequence labeling toolkit. arXiv preprint arXiv:1806.05626, 2018.
- ---实体识别(NAACL)---
- Ju M, Miwa M, Ananiadou S. A neural layered model for nested named entity recognition. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018, 1: 1446-1459.
- Wang Z, Qu Y, Chen L, et al. Label-aware double transfer learning for cross-specialty medical named entity recognition. NAACL2018.
- Moon S, Neves L, Carvalho V. Multimodal named entity recognition for short social ../media posts. NAACL2018.
- Katiyar A, Cardie C. Nested named entity recognition revisited. NAACL2018: 861-871.
- ---实体识别(EMNLP)---
- Cao P, Chen Y, Liu K, et al. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism.EMNLP2018: 182-192.
- Xie J, Yang Z, Neubig G, et al. Neural cross-lingual named entity recognition with minimal resources. EMNLP2018.
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- Greenberg N, Bansal T, Verga P, et al. Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets. EMNLP2018: 2824-2829.
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- Yu X, Mayhew S, Sammons M, et al. On the Strength of Character Language Models for Multilingual Named Entity Recognition. EMNLP2018.
- ---实体识别(COLING)---
- 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.
- 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.
- 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.
- 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.
- Güngör O, Üsküdarlı S, Güngör T. Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags. arXiv preprint arXiv:1807.06683, 2018.
- 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.
- Ghaddar A, Langlais P. Robust lexical features for improved neural network named-entity recognition. COLING2018.
- ---事件抽取(ACL)---
- 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.
- Lin H, Lu Y, Han X, et al. Nugget Proposal Networks for Chinese Event Detection. ACL2018.
- Huang L, Ji H, Cho K, et al. Zero-shot transfer learning for event extraction. ACL2017.
- Hong Y, Zhou W, Zhang J, et al. Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 515-526.
- Zhao Y, Jin X, Wang Y, et al. Document embedding enhanced event detection with hierarchical and supervised attention. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018, 2: 414-419.
- Yang H, Chen Y, Liu K, et al. DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data. ACL2018, System Demonstrations, 2018: 50-55.
- ---事件抽取(NAACL)---
- Ferguson J, Lockard C, Weld D S, et al. Semi-Supervised Event Extraction with Paraphrase Clusters. ACL2018.
- ---事件抽取(EMNLP)---
- Orr J W, Tadepalli P, Fern X. Event Detection with Neural Networks: A Rigorous Empirical Evaluation. EMNLP2018.
- Liu S, Cheng R, Yu X, et al. Exploiting Contextual Information via Dynamic Memory Network for Event Detection. EMNLP2018.
- Liu X, Luo Z, Huang H. Jointly multiple events extraction via attention-based graph information aggregation. EMNLP2018.
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- Lu W, Nguyen T H. Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4822-4828.
- ---事件抽取(COLING)---
- Araki J, Mitamura T. Open-Domain Event Detection using Distant Supervision. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 878-891.
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- Kazeminejad G, Bonial C, Brown S W, et al. Automatically Extracting Qualia Relations for the Rich Event Ontology. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2644-2652.
- Liu Z, Mitamura T, Hovy E. Graph-Based Decoding for Event Sequencing and Coreference Resolution. COLING2018.
- ---关系抽取---
- Su Y, Liu H, Yavuz S, et al. Global relation embedding for relation extraction, NAACL2018:820-830.
- Zeng X, He S, Liu K, et al. Large scaled relation extraction with reinforcement learning, AAAI2018.
- Liu T, Zhang X, Zhou W, et al. Neural relation extraction via inner-sentence noise reduction and transfer learning, EMNLP2018:2195-2204.
- Wang S, Zhang Y, Che W, et al. Joint Extraction of Entities and Relations Based on a Novel Graph Scheme, IJCAI2018: 4461-4467.
- Feng J, Huang M, Zhao L, et al. Reinforcement learning for relation classification from noisy data, AAAI2018.
- He Z, Chen W, Li Z, et al. SEE: Syntax-aware entity embedding for neural relation extraction, AAAI2018.
- Vashishth S , Joshi R , Prayaga S S , et al. RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information. ACL2018.
- Tan Z, Zhao X, Wang W, et al. Jointly Extracting Multiple Triplets with Multilayer Translation Constraints. AAAI2018.
- Ryuichi Takanobu, Tianyang Zhang, JieXi Liu, Minlie HuangA Hierarchical Framework for Relation Extraction with Reinforcement Learning, AAAI2019.
- ---知识存储---
- Davoudian A, Chen L, Liu M. A survey on NoSQL stores[J]. ACM Computing Surveys (CSUR), 2018, 51(2): 40.
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- Elzein N M, Majid M A, Hashem I A T, et al. Managing big RDF data in clouds: Challenges, opportunities, and solutions[J]. Sustainable Cities and Society, 2018, 39: 375-386.
- ---知识推理---
- Lin, Xi Victoria, Richard Socher, and Caiming Xiong. Multi-hop knowledge graph reasoning with reward shaping. arXiv preprint arXiv:1808.10568 (2018).
- Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April). Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Gu, L., Xia, Y., Yuan, X., Wang, C., & Jiao, J. (2018). Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph. Filomat, 32(5).
- Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April).Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017, August). Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 3462-3471). JMLR. org.
- Hamilton, W., Bajaj, P., Zitnik, M., Jurafsky, D., & Leskovec, J. (2018).Embedding logical queries on knowledge graphs. In Advances in Neural Information Processing Systems (pp. 2026-2037).
- ---实体链接---
- Sil, A., Kundu, G., Florian, R., & Hamza, W. (2018, April). Neural cross-lingual entity linking. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Chen, H., Wei, B., Liu, Y., Li, Y., Yu, J., & Zhu, W. (2018). Bilinear joint learning of word and entity embeddings for Entity Linking. Neurocomputing, 294, 12-18.
- Raiman, J. R., & Raiman, O. M. (2018, April). DeepType: multilingual entity linking by neural type system evolution. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Kundu, G., Sil, A., Florian, R., & Hamza, W. (2018). Neural cross-lingual coreference resolution and its application to entity linking. arXiv preprint arXiv:1806.10201.
- Kilias, T., Löser, A., Gers, F. A., Koopmanschap, R., Zhang, Y., & Kersten, M. (2018). Idel: In-database entity linking with neural embeddings. arXiv preprint arXiv:1803.04884.
- Cao, Y., Hou, L., Li, J., & Liu, Z. (2018). Neural collective entity linking. arXiv preprint arXiv:1811.08603.
-
---知识融合---
Wang, Zhichun, et al. Cross-lingual knowledge graph alignment via graph convolutional networks. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018. -
---知识补全---
Xiaolan Wang, Xin Luna Dong, Yang Li, Alexandra Meliou. MIDAS: Finding the right web sources to fill knowledge gaps. ICDE2019. -
---知识构建---
Nguyen D B, Abujabal A, Tran N K, et al. Query-driven on-the-fly knowledge base construction. VLDB2017. -
---知识评估---
Gao J, Li X, Xu Y E, et al. Efficient knowledge graph accuracy evaluation. VLDB2019, 12(11): 1679-1691. -
---知识图谱:综述、概念和发展---
Claudio Gutierrez and Juan F. Sequeda. A Brief History of Knowledge Graph's Main Ideas: A tutorial. ISWC2019 tutorial
Noy N, Gao Y, Jain A, et al. Industry-scale knowledge graphs: Lessons and challenges. ACM Queue, 2019, 17(2): 48-75.
Top-level Conference Publications on Knowledge Graph (2018-2020)
Stanford Spring 2021 《Knowledge Graphs》
Stanford Spring 2020 《Knowledge Graphs》