知识图谱选哪些方向好发论文？附60+篇经典论文合集
导读：知识图谱的概念是Google于2012年正式提出，但是知识图谱的发展却可以追溯到1960年的语义网络。
1984
Douglas Lenat设立的Cyc是本体知识库。
1989
Tim Berners-Lee发明了万维网。
1998
Tim Berners-Lee再次提出语义网，语义网是能够根据语义进行判断的智能网络，实现人与电脑之间的无障碍沟通。它好比一个巨型的大脑，智能化程度极高，协调能力非常强大。
2006
Tim Berners-Lee提出链接数据（Linked Data）的概念，数据不仅仅发布于语义网中，而要建立起数据之间的链接从而形成一张巨大的链接数据网。
2007
DBpedia项目是目前已知的第一个大规模开放域链接数据。
2012
Google提出了知识图谱的概念。
知识图谱大体可以分为三个方向，分别为知识表示、知识获取和知识应用，也有把时序知识单独分成一类的。
而在不同的方向中，又有很多细分的方向，如知识表示建模、实体识别、实体链接、关系抽取、事件抽取、信息抽取、知识表示、知识融合、知识推理、知识图谱嵌入等等。
▋由于方向很多，想发表知识图谱相关的论文常常无从下手，那么知识图谱现在选择哪个方向好发论文呢？
近期随着深度学习模型的发展，特别是基于Bert的预训练模型的发展，知识作为先验信息在自然语言理解中起着重要的作用，将知识图谱通过某种方式融入到预训练模型中，进而可以获得效果上的提升。
在预训练模型上引入知识的工作如ERNIE，K-BERT，KEPLER等，通过在已有模型中加入entity embedding输入或者objective function约束来引入知识。
相应也会有一些研究热点，如：
1️⃣以马尔可夫逻辑网、本体推理的联合推理方法；
2️⃣知识图谱与预训练模型的融合；
3️⃣跨语言的知识抽取方法；
4️⃣基于实体的、关系的、Web文本的、多知识库的融合方法；
5️⃣多模态知识图谱技术；
6️⃣图神经网络进行知识嵌入；
7️⃣强化学习与知识图谱的结合使用（图谱推理、实体对齐）；
8️⃣增量更新技术在知识图谱上的应用等等。
以上研究热点均可作为尝试发表论文的方向（tips：文末有参考论文）。
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目前，大规模知识图谱的应用场景和方式还比较有限，其在智能搜索、深度问答、社交网络以及其他行业中的使用也只是处于初级阶段，仍具有广阔的可扩展空间。
在挖掘需求、探索知识图谱的应用场景时，应充分考虑知识图谱的以下优势：
1) 对海量、异构、动态的半结构化、非结构化数据的有效组织与表达能力;
2) 依托于强大知识库的深度知识推理能力;
3) 与深度学习、类脑科学等领域 相结合，逐步扩展的认知能力。
在对知识图谱技术有丰富积累的基础上，敏锐的感知人们的需求，结合具体的业务，可为大规模知识图谱的应用找到更宽广、更合适的应用之道。
01
知识图谱构建
► Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. KDD2014: 601-610.
► Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge. WWW2007: 697-706.
► 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.
02
关系抽取
► 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.
► Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[J]. 2014.
► Santos, Cicero Nogueira dos, Bing Xiang, and Bowen Zhou. “Classifying relations by ranking with convolutional neural networks.” In Proceedings of ACL, 2015.
03
事件抽取
► Chen Y, Xu L, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks. ACL2015, 1: 167-176.
► Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks. ACL2015, 2: 365-371.
► Narasimhan K, Yala A, Barzilay R. Improving information extraction by acquiring external evidence with reinforcement learning. EMNLP2016.
► Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks. NAACL2016: 300-309.
04
知识融合
► 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.
► Zhang Y, Zhang F, Yao P, et al. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. KDD2018: 1002-1011.
► Ngomo A C N, Auer S. LIMES—a time-efficient approach for large-scale link discovery on the web of data. IJCAI2011.
05
知识图谱嵌入
► 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)
06
知识推理/知识挖掘
► 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.
► Shen Y , Huang P S , Chang M W , et al. Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion[J]. 2016.
► Graves A , Wayne G , Reynolds M , et al. Hybrid computing using a neural network with dynamic external memory[J]. Nature.
► Yang F , Yang Z , Cohen W W . Differentiable Learning of Logical Rules for Knowledge Base Reasoning[J]. 2017.
07
实体识别（ACL）
► 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.
► Xu H, Liu B, Shu L, et al. Double embeddings and cnn-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601, 2018.
► 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.
08
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.
09
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.
► Lin B Y, Lu W. Neural adaptation layers for cross-domain named entity recognition. EMNLP2018.
► Shang J, Liu L, Ren X, et al. Learning Named Entity Tagger using Domain-Specific Dictionary. EMNLP2018.
10
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.
11
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.
► 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.
12
事件抽取（NAACL）
► Ferguson J, Lockard C, Weld D S, et al. Semi-Supervised Event Extraction with Paraphrase Clusters. ACL2018.
13
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.
► Chen Y, Yang H, Liu K, et al. Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms. EMNLP2018: 1267-1276.
14
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.
► Muis A O, Otani N, Vyas N, et al. Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 70-82.
► 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.
15
关系抽取
► 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-Spervised 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.
16
知识存储
► Zhang X, Zhang M, Peng P, et al. A Scalable Sparse Matrix-Based Join for SPARQL Query Processing[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2019: 510-514.
► Libkin L, Reutter J L, Soto A, et al. TriAL: A navigational algebra for RDF triplestores[J]. ACM Transactions on Database Systems (TODS), 2018, 43(1): 5.
► 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.
17
知识推理
► 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).
18
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.
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