https://github.com/academic-hammer/kg-survey

Survey on Knowledge Graph

https://github.com/academic-hammer/kg-survey

Repository

Survey on Knowledge Graph

https://github.com/Academic-Hammer/KG-Survey/blob/master/

# KG-Survey
Survey on Knowledge Graph

## Paper List

### Entity Linking

- [Entity Linking for Queries by Searching Wikipedia Sentences](https://arxiv.org/pdf/1704.02788) [EMNLP2017, cites=2]

- [Entity Linking via Joint Encoding of Types , Descriptions , and Context](http://www.aclweb.org/anthology/D17-1284) [EMNLP2017, cites=14]

- [List-only Entity Linking](http://www.aclweb.org/anthology/P17-2085) [ACL2017, cites=6]

- [Improving Entity Linking by Modeling Latent Relations between Mentions](https://arxiv.org/pdf/1804.10637) [ACL2018]

- [Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking](http://www.aclweb.org/anthology/P18-1010) [ACL2018]

- [ELDEN : Improved Entity Linking using Densified Knowledge Graphs](http://www.aclweb.org/anthology/N18-1167) [NAACL2018, cites=1]

- [Pangloss: Fast Entity Linking in Noisy Text Environments](https://arxiv.org/pdf/1807.06036) [KDD2018]

- [Neural Collective Entity Linking](http://www.aclweb.org/anthology/C18-1057) [COLING2018]

- [Systematic Study of Long Tail Phenomena in Entity Linking](http://www.aclweb.org/anthology/C18-1056) [COLING2018]

- [They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking](http://www.aclweb.org/anthology/C18-1003) [COLING2018]

### Named Entity Recognition

- A survey of named entity recognition and classification. [cites=1691]

- A Survey on Recent Advances in Named Entity Recognition from Deep Learning models [COLING2018]

- A Study of the Importance of External Knowledge in the Named Entity Recognition Task [ACL2018]

- Exploiting Wikipedia as External Knowledge for Named Entity Recognition [EMNLP2007]

- Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation. [ACL2016]

- [DEEP ACTIVE LEARNING FOR NAMED ENTITY RECOGNITION](https://openreview.net/pdf?id=ry018WZAZ) [ICLR2018, cites=11]

- (Open source tool) [NCRF++: An Open-source Neural Sequence Labeling Toolkit](https://arxiv.org/pdf/1806.05626.pdf) [ACL2018, cites=4]

- [Empower Sequence Labeling with Task-Aware Neural Language Model](https://arxiv.org/pdf/1709.04109.pdf) [AAAI2018, cites=7]

- 1.[Named Entity Recognition With Parallel Recurrent Neural Networks](http://www.aclweb.org/anthology/P18-2012) [NAACL2018]

- 2.[Semi-supervised sequence tagging with bidirectional language models](https://arxiv.org/pdf/1705.00108.pdf) [ACL2017, cites=44]

- 3.[Named Entity Recognition with Bidirectional LSTM-CNNs](https://arxiv.org/pdf/1511.08308.pdf) [axiv2015, cites=261]

- 4.[Neural Architectures for Named Entity Recognition](https://arxiv.org/pdf/1603.01360.pdf) [NAACL2016, cites=560]

- 5.[Named Entity Recognition for Novel Types by Transfer Learning](http://www.aclweb.org/anthology/D/D16/D16-1087.pdf) [EMNLP2016, cites=11]

- 6.[Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks](http://www.aclweb.org/anthology/P/P16/P16-2039.pdf) [ACL2016, cites=6]

- 7.[Segment-Level Neural Conditional Random Fields for Named Entity Recognition](http://www.aclweb.org/anthology/I17-2017) [IJCNLP2017, cites=2]

- 8.[Multi-domain evaluation framework for named entity recognition tools](https://www.sciencedirect.com/science/article/pii/S0885230815300504) [Computer Speech & Language2017, cites=2]

- 9.[Deep learning with word embeddings improves biomedical named entity recognition](https://academic.oup.com/bioinformatics/article-abstract/33/14/i37/3953940) [Bioinformatics2017, cites=40]

- 10.[A combination of active learning and self-learning for named entity recognition on Twitter using conditional random fields](https://www.sciencedirect.com/science/article/pii/S0950705117303040) [KBS2017, cites=4]

- 11.[Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks](http://www.aclweb.org/anthology/D/D17/D17-1282.pdf) [EMNLP2017, cites=2]

- 12.[A Local Detection Approach for Named Entity Recognition and Mention Detection](http://www.aclweb.org/anthology/P/P17/P17-1114.pdf) [ACL2017, cites=9]

- 13.[Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection](http://www.aclweb.org/anthology/P/P17/P17-1135.pdf) (ACL2017, cites=5)

- 14.[A Neural Layered Model for Nested Named Entity Recognition](http://www.aclweb.org/anthology/N18-1131) [ACL2018, cites=0]

- 15.[Combining rule-based and statistical mechanisms for low-resource named entity recognition](https://www.researchgate.net/publication/321954024_Combining_rule-based_and_statistical_mechanisms_for_low-resource_named_entity_recognition) [MT2018, cites=1]

- 16.[Design Challenges and Misconceptions in Neural Sequence Labeling](https://arxiv.org/abs/1806.04470) [COLING2018, cites=2]
 
### Relation Extraction
1. Pengda Qin, Weiran XU, William Yang Wang. [Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](http://aclweb.org/anthology/P18-1199) ACL (2018).
2. Pengda Qin, Weiran XU, William Yang Wang. [DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction](http://aclweb.org/anthology/P18-1046) ACL (2018).
3. Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou. [A Walk-based Model on Entity Graphs for Relation Extraction](http://aclweb.org/anthology/P18-2014) ACL (2018).
4. Van-Thuy Phi, Joan Santoso, Masashi Shimbo, Yuji Matsumoto. [Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction](http://aclweb.org/anthology/P18-2015) ACL (2018).
5. Y Lin, S Shen, Z Liu, H Luan, M Sun. [Neural Relation Extraction with Selective Attention over Instances](http://aclweb.org/anthology/P/P16/P16-1200.pdf). ACL (2016).
6. X Zeng, S He, K Liu, J Zhao. [Large Scaled Relation Extraction with Reinforcement Learning](http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/zeng_aaai2018.pdf). AAAI (2018).
7. Yatian Shen, Xuanjing Huang. [Attention-Based Convolutional Neural Network for Semantic Relation Extraction](http://www.aclweb.org/anthology/C16-1238) COLING (2016).
8. D Zeng, K Liu, S Lai, G Zhou, J Zhao. [Relation classification via convolutional deep neural networks](http://www.aclweb.org/anthology/C14-1220). COLING (2014).
9. D Zeng, K Liu, Y Chen, J Zhao. [Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks](http://www.aclweb.org/anthology/D15-1203). ACL (2015). 
10. G Ji, K Liu, S He, J Zhao. [Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions](http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14491/14078). AAAI (2017).
11. TH Nguyen, R Grishman. [Relation ExtractionPerspective from Convolutional Neural Networks](http://www.aclweb.org/anthology/W15-1506).
12. D Zhang, D Wang. [Relation Classification via Recurrent Neural Network](https://arxiv.org/pdf/1508.01006). 2015
13. TH Nguyen, R Grishman. [Combining Neural Networks and Log-linear Models to Improve Relation Extraction](https://arxiv.org/pdf/1511.05926). 2015
14. MR Gormley, M Yu, M Dredze. [Improved Relation Extraction with Feature-Rich Compositional Embedding Models](https://arxiv.org/pdf/1505.02419). 2015
15. Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Tarek F. Abdelzaher, Jiawei Han. [CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases](http://cn.arxiv.org/pdf/1610.08763). WWW (2017).
16. Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao,Peng Zhou, Bo Xu. [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://arxiv.org/pdf/1706.05075).
17. Y Lin, Z Liu, M Sun. [Neural Relation Extraction with Multi-lingual Attention](http://www.aclweb.org/anthology/P17-1004). ACL (2017).
18. Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao. [Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix](https://arxiv.org/pdf/1705.03995). ACL (2017).
19. J Tourille, O Ferret, A Neveol, X Tannier. [Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers](http://www.aclweb.org/anthology/P17-2035). ACL (2017).
20. N Peng, H Poon, C Quirk, K Toutanova, et al. [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://arxiv.org/pdf/1708.03743) ACL (2017).
21. A Abad, M Nabi, A Moschitti. [Self-Crowdsourcing Training for Relation Extraction](http://www.aclweb.org/anthology/P17-2082). ACL (2017).
22. M Miwa, M Bansal. [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://arxiv.org/paf/1601.00770)
23. JP. CND Santos, B Xiang, B Zhou. [Classifying relations by ranking with convolutional neural networks](https://arxiv.org/pdf/1504.06580) [cites=119, ACL'2015].
24. JP. X Jiang, W Quan, L Peng, B Wang. [Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks](http://www.aclweb.org/anthology/C/C16/C16-1139.pdf) [cites=11 ,COLING'2016]
25. [Adversarial Training for Relation Extraction](http://aclweb.org/anthology/D17-1187) [EMNLP 2017]
26. [Open Relation Extraction and Grounding](http://aclweb.org/anthology/I17-1086) [IJCNLP 2017]
27. [Global Relation Embedding for Relation Extraction](http://aclweb.org/anthology/N18-1075) [NAACL 2018]
28. [Incorporating Relation Paths in Neural Relation Extraction](http://aclweb.org/anthology/D17-1186) [EMNLP 2017]
29. [Distant Supervision for Relation Extraction beyond the Sentence Boundary](http://aclweb.org/anthology/E17-1110) [EACL 2017]
30. [Relation extraction pattern ranking using word similarity](http://www.aclweb.org/anthology/N15-2004) [NAACL 2015]
31. [Reinforcement Learning for Relation Classification from Noisy Data](https://tianjun.me/static/essay_resources/RelationExtraction/Paper/AAAI2018Denoising.pdf)
### Common Paper
- [Conditional random fields: Probabilistic models for segmenting and labeling sequence data](https://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers) [2001, cites=11655]
- [Long short-term memory](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf) [Neural computation1991, cites=12498]
- [Imagenet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [NIPS2012, cites=28920]

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