A collection of knowledge graph papers, codes, and reading notes.
- Awesome Knowledge Graph
- Survey
- Papers by venues
- Knowledge Graph Embedding
- Cross-Modal KG Embedding
- Knowledge Acquisition
- Knowledge-aware Applications
- Temporal Knowledge Graph
- Knowledge Graph Reasoning
- One/few-Shot and Zero-Shot Learning
- Domain-specific Knowledge Graphs
- KG Database Systems
- Data
- Libraries, Softwares and Tools
- Courses, Tutorials and Seminars
- Related Repos
- Acknowledgements
A Survey on Knowledge Graphs: Representation, Acquisition and Applications. Preprint 2020. Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu. [Paper] [专知]
Knowledge Graphs. Preprint 2020. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann. [Paper] [专知]
Knowledge Representation Learning: A Quantitative Review. Preprint 2018. Lin, Yankai and Han, Xu and Xie, Ruobing and Liu, Zhiyuan and Sun, Maosong. [Paper]
Knowledge graph embedding: A survey of approaches and applications. TKDE 2017. Wang, Quan and Mao, Zhendong and Wang, Bin and Guo, Li. [Paper]
Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 2017. Paulheim, Heiko. [Paper]
A review of relational machine learning for knowledge graphs. Proceedings of the IEEE 2015. Nickel, Maximilian and Murphy, Kevin and Tresp, Volker and Gabrilovich, Evgeniy. [Paper]
| Year | WWW | AAAI | ACL |
|---|---|---|---|
| 2020 | 20 | 28 | 53 |
Relation Embedding with Dihedral Group in Knowledge Graph. ACL 2019. Xu, Canran and Li, Ruijiang. [Paper]
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. ICLR 2019. Zhiqing Sun and Zhi-Hong Deng and Jian-Yun Nie and Jian Tang. [Paper] [Code]
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding. AAAI 2019. Wang, Peifeng and Han, Jialong and Li, Chenliang and Pan, Rong. [Paper]
Embedding Uncertain Knowledge Graphs. AAAI 2019. Chen et al. [Paper]
TransGate: Knowledge Graph Embedding with Shared Gate Structure. AAAI 2019. Yuan et al. [Paper]
Improved Knowledge Graph Embedding using Background Taxonomic Information. AAAI 2019. Fatemi et al. [Paper]
Validation of Growing Knowledge Graphs by Abductive Text Evidences. AAAI 2019. Du et al. [Paper]
Variational Quantum Circuit Model for Knowledge Graph Embedding. Advanced Quantum Technologies 2019. Yunpu Ma, Volker Tresp, Liming Zhao, and Yuyi Wang. [Paper]
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs. WSDM 2019. Wen Zhang, Bibek Paudel, Wei Zhang, Abraham Bernstein, Huajun Chen. [Paper]
Does William Shakespeare Really Write Hamlet? Knowledge Representation Learning with Confidence. AAAI 2018. Ruobing Xie, Zhiyuan Liu, Fen Lin, and Leyu Lin. [Paper] [Code]
TorusE: Knowledge graph embedding on a lie group. AAAI 2018. Ebisu, Takuma and Ichise, Ryutaro. [Paper]
Convolutional 2d knowledge graph embeddings. AAAI 2018. Dettmers, Tim and Minervini, Pasquale and Stenetorp, Pontus and Riedel, Sebastian. [Paper]
Towards Understanding the Geometry of Knowledge Graph Embedding. ACL 2018. Chandrahas, Aditya Sharma and Partha Talukdar. [Paper] [Code] [Note]
Co-training Embedding of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment. IJCAI 2018, Chen, Muhao, Yingtao Tian, Kai-Wei Chang, Steven Skiena, and Carlo Zaniolo. [Paper] [Note]
Scalable Rule Learning via Learning Representation. IJCAI 2018. Omran, Pouya Ghiasnezhad, Kewen Wang, and Zhe Wang. [Paper] [Note]
KBGAN: Adversarial Learning for Knowledge Graph Embeddings. NAACL 2018. Cai, Liwei, and William Yang Wang. [Paper] [Code] [Note]
Embedding Logical Queries on Knowledge Graphs. NIPS 2018. William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec. [Paper] [Code]
SimpIE Embedding for Link Prediction in Knowledge Graphs. NIPS 2018. Seyed Mehran Kazemi, David Poole. [Paper] [Code]
Differentiating Concepts and Instances for Knowledge Graph Embedding. EMNLP 2018. Xin Lv, Lei Hou, Juanzi Li, Zhiyuan Liu. [Paper] [Code]
Analogical inference for multi-relational embeddings. ICML 2017. Liu, Hanxiao and Wu, Yuexin and Yang, Yiming. [Paper] [Code]
Poincaré embeddings for learning hierarchical representations. NIPS 2017. Nickel, Maximillian and Kiela.[Paper] [Code]
On the equivalence of holographic and complex embeddings for link prediction. ACL 2017. Hayashi, Katsuhiko and Shimbo, Masashi. [Paper]
Modeling relational data with graph convolutional networks. European Semantic Web Conference 2017. Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Van Den Berg, Rianne and Titov, Ivan and Welling, Max. [Paper]
Holographic embeddings of knowledge graphs. AAAI 2016. Nickel, Maximilian and Rosasco, Lorenzo and Poggio, Tomaso. [Paper]
Complex embeddings for simple link prediction. ICML 2016. Trouillon, Théo and Welbl, Johannes and Riedel, Sebastian and Gaussier, Éric and Bouchard, Guillaume. [Paper] [Code]
Embedding entities and relations for learning and inference in knowledge bases. ICLR 2015. Yang, Bishan and Yih, Wen-tau and He, Xiaodong and Gao, Jianfeng and Deng, Li. [Paper]
Effective blending of two and three-way interactions for modeling multi-relational data. ECML-KDD 2014. García-Durán, Alberto and Bordes, Antoine and Usunier, Nicolas. [Paper]
Relation extraction with matrix factorization and universal schemas. NAACL 2013. Riedel, Sebastian and Yao, Limin and McCallum, Andrew and Marlin, Benjamin M. [Paper]
A latent factor model for highly multi-relational data. NIPS 2012. Jenatton, Rodolphe and Roux, Nicolas L and Bordes, Antoine and Obozinski, Guillaume R. [Paper]
Factorizing YAGO: scalable machine learning for linked data. ICML 2012. Nickel, Maximilian and Tresp, Volker and Kriegel, Hans-Peter. [Paper]
A Three-Way Model for Collective Learning on Multi-Relational Data. ICML 2011. Nickel, Maximilian and Tresp, Volker and Kriegel, Hans-Peter. [Paper]
Modelling relational data using bayesian clustered tensor factorization. NIPS 2009. Sutskever, Ilya and Tenenbaum, Joshua B and Salakhutdinov, Ruslan R. [Paper]
Translating embeddings for modeling multi-relational data. NIPS 2013. Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana. [Paper]
Knowledge graph embedding by translating on hyperplanes. AAAI 2014. Wang, Zhen and Zhang, Jianwen and Feng, Jianlin and Chen, Zheng. [Paper]
Learning entity and relation embeddings for knowledge graph completion. AAAI 2015. Lin, Yankai and Liu, Zhiyuan and Sun, Maosong and Liu, Yang and Zhu, Xuan. [Paper] [Code]
STransE: a novel embedding model of entities and relationships in knowledge bases. NAACL 2016. Nguyen, Dat Quoc and Sirts, Kairit and Qu, Lizhen and Johnson, Mark. [Paper]
Knowledge graph embedding via dynamic mapping matrix. ACL 2015. Ji, Guoliang and He, Shizhu and Xu, Liheng and Liu, Kang and Zhao, Jun. [Paper]
A translation-based knowledge graph embedding preserving logical property of relations. NAACL 2016. Yoon, Hee-Geun and Song, Hyun-Je and Park, Seong-Bae and Park, Se-Young. [Paper]
Knowledge graph completion with adaptive sparse transfer matrix. AAAI 2016. Ji, Guoliang and Liu, Kang and He, Shizhu and Zhao, Jun. [Paper]
TransA: An adaptive approach for knowledge graph embedding. AAAI 2015. Xiao, Han and Huang, Minlie and Hao, Yu and Zhu, Xiaoyan. [Paper]
Knowledge graph embedding by flexible translation. KR 2016. Feng, Jun and Huang, Minlie and Wang, Mingdong and Zhou, Mantong and Hao, Yu and Zhu, Xiaoyan. [Paper]
Learning to represent knowledge graphs with gaussian embedding. CIKM 2015. He, Shizhu and Liu, Kang and Ji, Guoliang and Zhao, Jun. [Paper]
From one point to a manifold: Orbit models for knowledge graph embedding. IJCAI 2016. Xiao, Han and Huang, Minlie and Zhu, Xiaoyan. [Paper]
Composing relationships with translations. EMNLP 2015. García-Durán, Alberto and Bordes, Antoine and Usunier, Nicolas. [Paper] [Code]
A semantic matching energy function for learning with multi-relational data. Machine Learning 2014. Bordes, Antoine and Glorot, Xavier and Weston, Jason and Bengio, Yoshua. [Paper]
Knowledge graph and text jointly embedding. EMNLP 2015. Wang, Zhen and Zhang, Jianwen and Feng, Jianlin and Chen, Zheng. [Paper]
Aligning knowledge and text embeddings by entity descriptions. EMNLP 2015. Zhong, Huaping and Zhang, Jianwen and Wang, Zhen and Wan, Hai and Chen, Zheng. [Paper]
Joint semantic relevance learning with text data and graph knowledge. ACL-IJCNLP Workshop 2015. Zhang, Dongxu and Yuan, Bin and Wang, Dong and Liu, Rong. [Paper]
SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI 2017. Xiao, Han and Huang, Minlie and Meng, Lian and Zhu, Xiaoyan. [Paper]
Representation learning of knowledge graphs with entity descriptions. AAAI 2016. Xie, Ruobing and Liu, Zhiyuan and Jia, Jia and Luan, Huanbo and Sun, Maosong. [Paper] [Code]
Distributed representation learning for knowledge graphs with entity descriptions. PRL 2017. Fan, Miao and Zhou, Qiang and Zheng, Thomas Fang and Grishman, Ralph. [Paper]
Text-enhanced representation learning for knowledge graph. IJCAI 2016. Wang, Zhigang and Li, Juan-Zi. [Paper]
Knowledge Representation Learning with Entities, Attributes and Relations. IJCAI 2016. [Paper] [Code]
Type-constrained representation learning in knowledge graphs. ISWC 2015. Krompass, Denis and Baier, Stephan and Tresp, Volker. [Paper]
Typed tensor decomposition of knowledge bases for relation extraction. EMNLP 2014. Chang, Kai-Wei and Yih, Wen-tau and Yang, Bishan and Meek, Christopher. [Paper]
Semantically smooth knowledge graph embedding. ACL 2015. Guo, Shu and Wang, Quan and Wang, Bin and Wang, Lihong and Guo, Li. [Paper]
Entity hierarchy embedding. ACL 2015. Hu, Zhiting and Huang, Poyao and Deng, Yuntian and Gao, Yingkai and Xing, Eric. [Paper]
Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI 2016. Xie, Ruobing and Liu, Zhiyuan and Sun, Maosong. [Paper]
Knowledge graph embedding with hierarchical relation structure. EMNLP 2018. Zhang, Zhao and Zhuang, Fuzhen and Qu, Meng and Lin, Fen and He, Qing. [Paper]
AMIE: association rule mining under incomplete evidence in ontological knowledge bases. WWW 2013. Galárraga, Luis Antonio and Teflioudi, Christina and Hose, Katja and Suchanek, Fabian. [Paper)]
Knowledge graph identification. ISWC 2013. Pujara, Jay and Miao, Hui and Getoor, Lise and Cohen, William. [Paper]
Knowledge base completion using embeddings and rules. IJCAI 2015. Wang, Quan and Wang, Bin and Guo, Li. [Paper]
Injecting logical background knowledge into embeddings for relation extraction. NAACL 2015. Rocktäschel, Tim and Singh, Sameer and Riedel, Sebastian. [Paper]
Low-Dimensional Embeddings of Logic. ACL 2014. Rocktäschel, Tim and Bošnjak, Matko and Singh, Sameer and Riedel, Sebastian. [Paper]
Learning first-order logic embeddings via matrix factorization. IJCAI 2016. Wang, William Yang and Cohen, William W. [Paper]
Jointly embedding knowledge graphs and logical rules. EMNLP 2016. Guo, Shu and Wang, Quan and Wang, Lihong and Wang, Bin and Guo, Li. [Paper]
Knowledge Graph Embedding with Iterative Guidance from Soft Rules. AAAI 2018. Guo, Shu and Wang, Quan and Wang, Lihong and Wang, Bin and Guo, Li. [Paper] [Code]
Lifted rule injection for relation embeddings. EMNLP 2016. Demeester, Thomas and Rocktäschel, T and Riedel, S. [Paper]
Random walk inference and learning in a large scale knowledge base. EMNLP 2011. Lao, Ni and Mitchell, Tom and Cohen, William W. [Paper]
Relational retrieval using a combination of path-constrained random walks. Machine Learning 2010. Lao, Ni and Cohen, William W. [Paper]
Modeling relation paths for representation learning of knowledge bases. EMNLP 2015. Lin, Yankai and Liu, Zhiyuan and Luan, Huanbo and Sun, Maosong and Rao, Siwei and Liu, Song. [Paper] [Code]
Context-dependent knowledge graph embedding. EMNLP 2015. Luo, Yuanfei and Wang, Quan and Wang, Bin and Guo, Li. [Paper]
Compositional learning of embeddings for relation paths in knowledge base and text. ACL 2016. Toutanova, Kristina and Lin, Victoria and Yih, Wen-tau and Poon, Hoifung and Quirk, Chris. [Paper]
GAKE: graph aware knowledge embedding. COLING 2016. Feng, Jun and Huang, Minlie and Yang, Yang and Zhu, Xiaoyan. [Paper]
Traversing Knowledge Graphs in Vector Space. EMNLP 2015. Guu, Kelvin and Miller, John and Liang, Percy. [Paper]
Image-embodied knowledge representation learning. IJCAI 2017. Xie, Ruobing and Liu, Zhiyuan and Luan, Huanbo and Sun, Maosong. [Paper]
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs. Automated Knowledge Base Construction 2019. Oñoro-Rubio et al. [Paper] ⬆
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction WWW 2020. Rosso et al. [Paper]
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases WWW 2020. Liu et al.. [Paper]
Relation Adversarial Network for Low Resource Knowledge Graph Completion WWW 2020. Zhang et al. [Paper]
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning WWW 2020. He et al. [Paper]
Open Knowledge Enrichment for Long-tail Entities. WWW 2020. Cao et al.. [Paper] [Code]
LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion. AAAI 2019. Kong et al. [Paper] [Code]
On Completing Sparse Knowledge Graph with Transitive Relation Embedding. AAAI 2019. Zhou et al. [Paper]
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. AAAI 2019. Shang, Chao and Tang, Yun and Huang, Jing and Bi, Jinbo and He, Xiaodong and Zhou, Bowen. [Paper]
An Open-World Extention to Knowledge Graph Completion MOdels. AAAI 2019. Haseeb Shah, Johannes Villmow, Adrian Ulges, Ulrich Schwanecke, Faisal Shafait. [Paper] [Code]
Embedding Multimodal Relational Data for Knowledge Base Completion. EMNLP 2018. Pezeshkpour, Pouya, Liyan Chen, and Sameer Singh. [Paper] [Code] [Note]
Shared Embedding Based Neural Networks for Knowledge Graph Completion. CIKM 2018. Guan, Saiping and Jin, Xiaolong and Wang, Yuanzhuo and Cheng, Xueqi. [Paper]
Expanding Holographic Embeddings for Knowledge Completion. NIPS 2018. Yexiang Xue, Yang Yuan, Zhitian Xu, and Ashish Sabharwal. [Paper]
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search. NIPS 2018. Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao. [Paper]
Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism. IJCAI 2018. Qian, Wei and Fu, Cong and Zhu, Yu and Cai, Deng and He, Xiaofei. [Paper]
Compositional Vector Space Models for Knowledge Base Completion. ACL-IJCNLP 2015. Neelakantan, Arvind and Roth, Benjamin and McCallum, Andrew. [Paper]
An Interpretable Knowledge Transfer Model for Knowledge Base Completion. ACL 2017. Xie, Qizhe and Ma, Xuezhe and Dai, Zihang and Hovy, Eduard. [Paper]
A novel embedding model for knowledge base completion based on convolutional neural network. NAACL 2018. Nguyen, Dai Quoc and Nguyen, Tu Dinh and Nguyen, Dat Quoc and Phung, Dinh. [Paper]
ProjE: Embedding projection for knowledge graph completion. AAAI 2017. Shi, Baoxu and Weninger, Tim. [Paper] [Code]
TuckER: Tensor Factorization for Knowledge Graph Completion. Balažević, Ivana and Allen, Carl and Hospedales, Timothy M. [Paper] [Code]
Open-world knowledge graph completion. AAAI 2018. Shi, Baoxu and Weninger, Tim. [Paper] [Code]
On Multi-Relational Link Prediction with Bilinear Models. AAAI 2018. Wang, Yanjie and Gemulla, Rainer and Li, Hui. [Paper] [Code]
Inferring missing entity type instances for knowledge base completion: New dataset and methods. NAACL 2016. Neelakantan, Arvind and Chang, Ming-Wei. [Paper]
Spring-Electrical Models For Link Prediction. WSDM 2019. Kashinskaya, Yana and Samosvat, Egor and Artikov, Akmal. [Paper]
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization WWW 2020. Belth et al. [Paper]
Correcting Knowledge Base Assertions. WWW 2020. Chen et al.. [Paper]
Expanding Taxonomies with Implicit Edge Semantics WWW 2020. Manzoor et al. [Paper]
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction WWW 2020. Zhou et al.. [Paper]
LOREM: Language-consistent Open Relation Extraction from Unstructured Text. WWW 2020. Harting et al. [Paper]
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. NAACL 2019. Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen. [Paper]
Discovering Correlations between Sparse Features in Distant Supervision for Relation Extraction. WSDM 2019. Qu, Jianfeng and Ouyang, Dantong and Hua, Wen and Ye, Yuxin and Zhou, Xiaofang. [Paper]
A Hierarchical Framework for Relation Extraction with Reinforcement Learning. AAAI 2019. Takanobu, Ryuichi and Zhang, Tianyang and Liu, Jiexi and Huang, Minlie. [Paper] [Code]
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning. EMNLP 2018. Liu, Tianyi, Xinsong Zhang, Wanhao Zhou, and Weijia Jia. [Paper] [Note]
DSGAN: Generative Adversarial Training for Robust Distant Supervision Relation Extraction. ACL 2018. Pengda Qin, Weiran Xu, William Yang Wang. [Paper]
Deep Residual Learning for Weakly-Supervised Relation Extraction. EMNLP 2017. Yi Yao Huang, William Yang Wang. [Paper] [Code]
Incorporating Relation Paths in Neural Relation Extraction. EMNLP 2017. Zeng, Wenyuan and Lin, Yankai and Liu, Zhiyuan and Sun, Maosong. [Paper] [Code]
Knowledge-based weak supervision for information extraction of overlapping relations. ACL 2011. Hoffmann, Raphael and Zhang, Congle and Ling, Xiao and Zettlemoyer, Luke and Weld, Daniel S. [Paper]
Learning syntactic patterns for automatic hypernym discovery. NIPS 2005. Snow, Rion and Jurafsky, Daniel and Ng, Andrew Y. [Paper]
Distant supervision for relation extraction without labeled data. ACL 2009. Mintz, Mike and Bills, Steven and Snow, Rion and Jurafsky, Dan. [Paper]
Modeling relations and their mentions without labeled text. ECML 2010. Riedel, Sebastian and Yao, Limin and McCallum, Andrew. [Paper]
Multi-instance multi-label learning for relation extraction. EMNLP 2012. Surdeanu, Mihai and Tibshirani, Julie and Nallapati, Ramesh and Manning, Christopher D. [Paper]
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction. EMNLP 2013. Weston, Jason and Bordes, Antoine and Yakhnenko, Oksana and Usunier, Nicolas. [Paper]
Distant supervision for relation extraction with matrix completion. ACL 2014. Fan, Miao and Zhao, Deli and Zhou, Qiang and Liu, Zhiyuan and Zheng, Thomas Fang and Chang, Edward Y. [Paper]
Semantic compositionality through recursive matrix-vector spaces. EMNLP 2012. Socher, Richard and Huval, Brody and Manning, Christopher D and Ng, Andrew Y. [Paper]
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. ACL 2016. Miwa, Makoto and Bansal, Mohit. [Paper]
Relation Classification via Convolutional Deep Neural Network. COLING 2014. Zeng, Daojian and Liu, Kang and Lai, Siwei and Zhou, Guangyou and Zhao, Jun. [Paper]
Classifying relations via long short term memory networks along shortest dependency paths. EMNLP 2015. Xu, Yan and Mou, Lili and Li, Ge and Chen, Yunchuan and Peng, Hao and Jin, Zhi. [Paper]
Classifying Relations by Ranking with Convolutional Neural Networks. ACL 2015. dos Santos, Cicero and Xiang, Bing and Zhou, Bowen. [Paper]
Distant supervision for relation extraction via piecewise convolutional neural networks. EMNLP 2015. Zeng, Daojian and Liu, Kang and Chen, Yubo and Zhao, Jun. [Paper]
Neural relation extraction with selective attention over instances. ACL 2016. Lin, Yankai and Shen, Shiqi and Liu, Zhiyuan and Luan, Huanbo and Sun, Maosong. [Paper] [Code]
Adversarial training for relation extraction. EMNLP 2017. Wu, Yi and Bamman, David and Russell, Stuart. [Paper] [Code]
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks. COLING 2016. Jiang, Xiaotian and Wang, Quan and Li, Peng and Wang, Bin. [Paper]
Jointly Extracting Relations with Class Ties via Effective Deep Ranking. ACL 2017. Ye, Hai and Chao, Wenhan and Luo, Zhunchen and Li, Zhoujun [Paper]
A soft-label method for noise-tolerant distantly supervised relation extraction. EMNLP 2017. Liu, Tianyu and Wang, Kexiang and Chang, Baobao and Sui, Zhifang. [Paper]
Distant supervision for relation extraction with sentence-level attention and entity descriptions. AAAI 2017. Ji, Guoliang and Liu, Kang and He, Shizhu and Zhao, Jun. [Paper]
Attention-based convolutional neural network for semantic relation extraction. COLING 2016. Shen, Yatian and Huang, Xuanjing. [Paper]
Neural knowledge acquisition via mutual attention between knowledge graph and text. AAAI 2018. Han, Xu and Liu, Zhiyuan and Sun, Maosong. [Paper] [Code] Also for KGC.
Cooperative Denoising for Distantly Supervised Relation Extraction. COLING 2018. Lei, Kai and Chen, Daoyuan and Li, Yaliang and Du, Nan and Yang, Min and Fan, Wei and Shen, Ying. [Paper]
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention. EMNLP 2018. Han, Xu and Yu, Pengfei and Liu, Zhiyuan and Sun, Maosong and Li, Peng. [Paper] [Code]
Large scaled relation extraction with reinforcement learning. AAAI 2018. Zeng, Xiangrong and He, Shizhu and Liu, Kang and Zhao, Jun. [Paper]
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. ACL 2018. Qin, Pengda and Weiran, XU and Wang, William Yang. [Paper]
Incorporating vector space similarity in random walk inference over knowledge bases. EMNLP 2014. Gardner, Matt and Talukdar, Partha and Krishnamurthy, Jayant and Mitchell, Tom. [Paper]
Bidirectional recurrent convolutional neural network for relation classification. ACL 2016. Cai, Rui and Zhang, Xiaodong and Wang, Houfeng. [Paper]
Attention-based bidirectional long short-term memory networks for relation classification. ACL 2016. Zhou, Peng and Shi, Wei and Tian, Jun and Qi, Zhenyu and Li, Bingchen and Hao, Hongwei and Xu, Bo. [Paper]
Reinforcement learning for relation classification from noisy data. AAAI 2018. Feng, Jun and Huang, Minlie and Zhao, Li and Yang, Yang and Zhu, Xiaoyan. [Paper]
Multi-grained Named Entity Recognition. ACL 2019. Xia, Congying and Zhang, Chenwei and Yang, Tao and Li, Yaliang and Du, Nan and Wu, Xian and Fan, Wei and Ma, Fenglong and Philip, S Yu. [Paper]
Neural architectures for named entity recognition. NAACL 2017. Lample, Guillaume and Ballesteros, Miguel and Subramanian, Sandeep and Kawakami, Kazuya and Dyer, Chris. [Paper] [Cdde] [Code]
Named entity recognition with bidirectional LSTM-CNNs. TACL 2016. Chiu, Jason PC and Nichols, Eric. [Paper]
Novel Entity Discovery from Web Tables WWW 2020. Zhang et al. [Paper]
MetaNER: Named Entity Recognition with Meta-Learning WWW 2020. Li et al. [Paper]
Collective Multi-type Entity Alignment Between Knowledge Graphs WWW 2020. Zhu et al. [Paper]
Entity Alignment between Knowledge Graphs Using Attribute Embeddings. AAAI 2019. Trsedya, Bayu Distiawan and Qi, Jianzhong and Zhang, Rui. [Paper] [Code]
Multi-view Knowledge Graph Embedding for Entity Alignment. IJCAI 2019. Zhang, Qingheng and Sun, Zequn and Hu, Wei and Chen, Muhao and Guo, Lingbing and Qu, Yuzhong. [Paper] [Code]
Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. IJCAI 2018. Chen, Muhao and Tian, Yingtao and Chang, Kai-Wei and Skiena, Steven and Zaniolo, Carlo. [Paper]
Bootstrapping Entity Alignment with Knowledge Graph Embedding. IJCAI 2018. Zequn Sun, Wei Hu, Qingheng Zhang and Yuzhong Qu. [Paper] [Code] [Note]
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. EMNLP 2018. Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. [Paper]
Cross-lingual entity alignment via joint attribute-preserving embedding. ISWC 2017. Sun, Zequn and Hu, Wei and Li, Chengkai. [Paper]
Iterative entity alignment via joint knowledge embeddings. IJCAI 2017. Zhu, Hao and Xie, Ruobing and Liu, Zhiyuan and Sun, Maosong. [Paper] [Code]
Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. IJCAI 2017. Chen, Muhao and Tian, Yingtao and Yang, Mohan and Zaniolo, Carlo. [Paper]
Aligning Knowledge Base and Document Embedding Models Using Regularized Multi-Task Learning. ISWC 2018. Baumgartner, Matthias and Zhang, Wen and Paudel, Bibek and Dell’Aglio, Daniele and Chen, Huajun and Bernstein, Abraham. [Paper]
High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking WWW 2020. Fang et al. [Paper]
Dynamic Graph Convolutional Networks for Entity Linking WWW 2020. Wu et al. [Paper]
Improving Entity Linking by Modeling Latent Relations between Mentions. ACL 2018. Le, Phong and Titov, Ivan. [Paper]
Deep Joint Entity Disambiguation with Local Neural Attention. EMNLP 2017. Ganea, Octavian-Eugen and Hofmann, Thomas. [Paper]
Design challenges for entity linking. TACL 2015. Ling, Xiao and Singh, Sameer and Weld, Daniel S. [Paper]
Leveraging deep neural networks and knowledge graphs for entity disambiguation. 2015. Huang, Hongzhao and Heck, Larry and Ji, Heng. [Paper]
Entity disambiguation by knowledge and text jointly embedding. CoNLL 2016. Fang, Wei and Zhang, Jianwen and Wang, Dilin and Chen, Zheng and Li, Ming. [Paper]
End-to-End Neural Entity Linking. CoNLL 2018. Nikolaos Kolitsas, Octavian-Eugen Ganea, Thomas Hofmann [Paper]
Label noise reduction in entity typing by heterogeneous partial-label embedding. KDD 2016. Ren, Xiang and He, Wenqi and Qu, Meng and Voss, Clare R and Ji, Heng and Han, Jiawei. [Paper]
Label Embedding for Zero-shot Fine-grained Named Entity Typing. COLING 2016. Yukun Ma, Erik Cambria, Sa Gao. [Paper] [Code]
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference. AAAI 2019. Yoshikawa et al. [Paper] [Code]
Deep Short Text Classification with Knowledge Powered Attention. AAAI 2019. Chen et al. [Paper]
A neural knowledge language model. 2016. Ahn, Sungjin and Choi, Heeyoul and Pärnamaa, Tanel and Bengio, Yoshua. [Paper] [Dataset]
ERNIE: Enhanced Language Representation with Informative Entities. ACL 2019. Zhang, Zhengyan and Han, Xu and Liu, Zhiyuan and Jiang, Xin and Sun, Maosong and Liu, Qun. [Paper] [Code]
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM. AAAI 2018. Yukun Ma, Haiyun Peng, Erik Cambria. [Paper]
Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification. IJCAI 2017. Wang, Jin and Wang, Zhongyuan and Zhang, Dawei and Yan, Jun. [Paper]
Story Ending Generation with Incremental Encoding and Commonsense Knowledge. AAAI 2019. Jian Guan, Yansen Wang, Minlie Huang. [Paper]
Incorporating Commonsense Knowledge for Story Completion. AAAI 2019. Chen et al.. [Paper]
Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases. AAAI 2019. Sharma et al. [Paper]
Complex Factoid Question Answering with a Free-Text Knowledge Graph WWW 2020. Zhao et al. [Paper]
Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming. AAAI 2019. Mitra et al. [Paper]
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering. AAAI 2019. Deng et al. [Paper]
An Interpretable Reasoning Network for Multi-Relation Question Answering. COLING 2018. Zhou, Mantong and Huang, Minlie and Zhu, Xiaoyan. [Paper]
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base. NIPS 2018. Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin. [Paper] [Code]
Commonsense for Generative Multi-hop Question Answering Tasks. EMNLP 2018. Bauer, Lisa, Yicheng Wang, and Mohit Bansal. [Paper] [Code] [Note]
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph. AAAI 2018. Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar. [Paper]
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs. ISWC 2018. Dubey, Mohnish, Debayan Banerjee, Debanjan Chaudhuri, and Jens Lehmann. [Paper] [Code] [Note]
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases. COLING 2018. Hao, Yanchao, Hao Liu, Shizhu He, Kang Liu, and Jun Zhao. [Paper] [Note]
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks. NAACL 2018. Mohammed, Salman, Peng Shi, and Jimmy Lin. [Paper] [Code] [Note]
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base. ACL 2018. Gupta, Vishal, Manoj Chinnakotla, and Manish Shrivastava. [Paper] [Note]
TEQUILA: Temporal Question Answering over Knowledge Bases. CIKM 2018. Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Strötgen, Gerhard Weikum. [Paper]
Question answering over knowledge base with neural attention combining global knowledge information. 2016. Zhang et al.. [Paper]
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases. ACL 2016. Dai et al.. [Paper]
Efficiently answering technical questions—a knowledge graph approach. AAAI 2017. Yang et al.. [Paper]
An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. ACL 2017. Hao et al.. [Paper]
Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning. ACL 2017. He et al.. [Paper]
Neural Generative Question Answering. IJCAI 2016. Yin, Jun and Jiang, Xin and Lu, Zhengdong and Shang, Lifeng and Li, Hang and Li, Xiaoming. [Paper] [Dataset]
Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI 2018. Zhou, Hao, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. [Paper] [Code] [Note]
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. CoNLL 2018. Chaudhuri, Debanjan and Kristiadi, Agustinus and Lehmann, Jens and Fischer, Asja. [Paper]
Incorporating loose-structured knowledge into conversation modeling via recall-gate LSTM. IJCNN 2017. Xu, Zhen and Liu, Bingquan and Wang, Baoxun and Sun, Chengjie and Wang, Xiaolong. [Paper]
Lstm-based mixture-of-experts for knowledge-aware dialogues. ACL 2016 Workshop. Le, Phong and Dymetman, Marc and Renders, Jean-Michel. [Paper]
Flexible end-to-end dialogue system for knowledge grounded conversation. 2017. Zhu, Wenya and Mo, Kaixiang and Zhang, Yu and Zhu, Zhangbin and Peng, Xuezheng and Yang, Qiang. [Paper]
Reinforced Negative Sampling over Knowledge Graph for Recommendation WWW 2020. Wang et al. [Paper]
KGAT: Knowledge Graph Attention Network for Recommendation. KDD 2019. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu and Tat-Seng Chua. [Paper] [Code]
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW 2019. Wang, Hongwei, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. [Paper] [Code]
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference. WWW 2019. Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua. [Paper] [Code]
Explianable Reasoning over Knowledge Graphs for Recommendation. AAAI 2019. Wang, Xiang and Wang, Dingxian and Xu, Canran and He, Xiangnan and Cao, Yixin and Chua, Tat-Seng. [Paper] [Code]
Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems. TOIS 2019. Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. [Paper]
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM 2018. Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. [Paper]
Collaborative knowledge base embedding for recommender systems. SIGKDD 2016. Zhang, Fuzheng and Yuan, Nicholas Jing and Lian, Defu and Xie, Xing and Ma, Wei-Ying. [Paper]
Top-n recommendations from implicit feedback leveraging linked open data. RecSys 2013. Ostuni, Vito Claudio and Di Noia, Tommaso and Di Sciascio, Eugenio and Mirizzi, Roberto. [Paper]
dbrec—music recommendations using DBpedia. ISWC 2010. Passant, Alexandre. [Paper]
Collaborative knowledge base embedding for recommender systems. KDD 2016. Zhang, Fuzheng and Yuan, Nicholas Jing and Lian, Defu and Xie, Xing and Ma, Wei-Ying. [Paper]
Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings WWW 2020. Shi et al. [Paper]
Explicit semantic ranking for academic search via knowledge graph embedding. WWW 2017. Xiong et al. [Paper]
Joint event extraction via recurrent neural networks. NAACL 2016. Nguyen, Thien Huu and Cho, Kyunghyun and Grishman, Ralph. [Paper]
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. IJCAI 2019. H. Peng, J. Li, Q. Gong, Y. Song, Y. Ning, K. Lai, and P.S. Yu. [Paper]
TransNet: Translation-Based Network Representation Learning for Social Relation Extraction. IJCAI 2017. Tu, Cunchao and Zhang, Zhengyan and Liu, Zhiyuan and Sun, Maosong. [Paper]
Retrieving Textual Evidence for Knowledge Graph Facts. ESWC 2019. Gonenc Ercan, Shady Elbassuoni, and Katja Hose. [Paper]
Constructing biomedical domain-specific knowledge graph with minimum supervision KAIS 2019. Yuan, Jianbo and Jin, Zhiwei and Guo, Han and Jin, Hongxia and Zhang, Xianchao and Smith, Tristram and Luo, Jiebo. [Paper]
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. AAAI 2019. Li, Christy Y and Liang, Xiaodan and Hu, Zhiting and Xing, Eric P. [Paper]
On the Generative Discovery of Structured Medical Knowledge. KDD 2018. C. Zhang, Y. Li, N. Du, W. Fan, and P.S. Yu [Paper]
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention. WWW 2019. Gaur, Manas, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randon S. Welton, and Jyotishman Pathak. [Paper]
HDSKG: harvesting domain specific knowledge graph from content of webpages. SANER 2017. Zhao, Xuejiao and Xing, Zhenchang and Kabir, Muhammad Ashad and Sawada, Naoya and Li, Jing and Lin, Shang-Wei. [Paper]
Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph. CSME 2018. Li, Hongwei and Li, Sirui and Sun, Jiamou and Xing, Zhenchang and Peng, Xin and Liu, Mingwei and Zhao, Xuejiao. [Paper]
DeepWeak: reasoning common software weaknesses via knowledge graph embedding. SANER 2018. Han, Zhuobing and Li, Xiaohong and Liu, Hongtao and Xing, Zhenchang and Feng, Zhiyong. [Paper]
The structure and dynamics of knowledge network in domain-specific Q&A sites: a case study of stack overflow. Empirical Software Engineering 2017. Ye, Deheng and Xing, Zhenchang and Kapre, Nachiket [Paper]
Predicting semantically linkable knowledge in developer online forums via convolutional neural network. ICASE 2016. Xu, Bowen and Ye, Deheng and Xing, Zhenchang and Xia, Xin and Chen, Guibin and Li, Shanping. [Paper]
Mining Analogical Libraries in Q&A Discussions — Incorporating Relational and Categorical Knowledge into Word Embedding. SANER 2016. Chunyang Chen, Sa Gao, and Zhenchang Xing. [Paper]
TechLand: Assisting technology landscape inquiries with insights from stack overflow. ICSME 2016. Chen, Chunyang and Xing, Zhenchang and Han, Lei. [Paper]
The More You Know: Using Knowledge Graphs for Image Classification. CVPR 2017. Marino, Kenneth and Salakhutdinov, Ruslan and Gupta, Abhinav. [Paper]
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs. AAAI 2019. Gao et al. [Paper] [Code]
WK-VQA: World Knowledge-enabled Visual Question Answering Shah et al. [Paper]
Jointly Modeling Inter-Slot Relations by Random Walk on Knowledge Graphs for Unsupervised Spoken Language Understanding. NAACL-HLT 2015. Yun-Nung Chen, William Yang Wang, Alex Rudnicky. [Paper]
Hybrid Knowledge Routed Modules for Large-scale Object Detection. NIPS 2018. Chenhan Jiang, Hang Xu, Xiaodan Liang, and Liang Lin. [Paper] [Code]
Knowledge questions from knowledge graphs. SIGIR 2016. Seyler, Dominic and Yahya, Mohamed and Berberich, Klaus. [Paper]
One for All: Neural Joint Modeling of Entities and Events. AAAI 2019. Nguyen, Trung Minh and Nguyen, Thien Huu. [Paper]
Linking named entities in tweets with knowledge base via user interest modeling. KDD 2013. Shen, Wei and Wang, Jianyong and Luo, Ping and Wang, Min. [Paper]
Knowledge Aware Conversation Generation with Reasoning on Augmented Graph. 2019. Zhibin Liu, Zheng-Yu Niu, Hua Wu, Haifeng Wang. [Paper]
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP 2018. Dasgupta, Shib Sankar, Swayambhu Nath Ray, and Partha Talukdar. [Paper] [Code] [Note]
Learning Sequence Encoders for Temporal Knowledge Graph Completion. EMNLP 2018. Garcia-Duran, Alberto and Dumančić, Sebastijan and Niepert, Mathias. [Paper] [Code]
Towards time-aware knowledge graph completion. COLING 2016. Jiang, Tingsong and Liu, Tianyu and Ge, Tao and Sha, Lei and Chang, Baobao and Li, Sujian and Sui, Zhifang. [Paper]
Deriving validity time in knowledge graph. WWW Workshop 2018. Leblay, Julien and Chekol, Melisachew Wudage. [Paper]
Recurrent Event Network for Reasoning over Temporal Knowledge Graphs. ICLR 2019 Workshop. Jin, Woojeong and Zhang, Changlin and Szekely, Pedro and Ren, Xiang. [Paper] [Code]
Encoding temporal information for time-aware link prediction. EMNLP 2016. Jiang, Tingsong and Liu, Tianyu and Ge, Tao and Sha, Lei and Li, Sujian and Chang, Baobao and Sui, Zhifang. [Paper]
Predicting the co-evolution of event and knowledge graphs. FUSION 2016. Estóeban, Cristobal and Tresp, Volker and Yang, Yinchong and Baier, Stephan and Krompaß, Denis. [Paper]
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. ICML 2017. Trivedi, Rakshit and Dai, Hanjun and Wang, Yichen and Song, Le. [Paper]
Acquiring temporal constraints between relations. CIKM 2012. Talukdar, Partha Pratim and Wijaya, Derry and Mitchell, Tom. [Paper]
Coupled temporal scoping of relational facts. WSDM 2012. Talukdar, Partha Pratim and Wijaya, Derry and Mitchell, Tom. [Paper]
Dense event ordering with a multi-pass architecture. TACL 2014. Chambers, Nathanael and Cassidy, Taylor and McDowell, Bill and Bethard, Steven. [Paper]
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs. NeurIPS 2019. Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang. [Paper] [Code]
Chain of Reasoning for Visual Question Answering. NIPS 2018. Wu, Chenfei and Liu, Jinlai and Wang, Xiaojie and Dong, Xuan. [Paper]
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. NIPS 2018. Medhini Narasimhan, Svetlana Lazebnik, Alex Schwing. [Paper]
Symbolic Graph Reasoning Meets Convolutions. NIPS 2018. Xiaodan Liang, Zhiting HU, Hao Zhang, Liang Lin, and Eric P. Xing. [Paper]
Variational Knowledge Graph Reasoning. NAACL-HLT 2018. Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Yang Wang. [Paper]
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. EMNLP 2017. Wenhan Xiong, Thien Hoang, William Yang Wang. [Paper] [Code]
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision. ACL 2017. Liang, Chen and Berant, Jonathan and Le, Quoc and Forbus, Kenneth D and Lao, Ni. [Paper]
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic. MLJ 2015. William Yang Wang, Kathryn Mazaitis, Ni Lao, William W. Cohen. [Paper] [Code]
Reasoning with neural tensor networks for knowledge base completion. NIPS 2013. Socher, Richard and Chen, Danqi and Manning, Christopher D and Ng, Andrew. [Paper]
Probabilistic reasoning via deep learning: Neural association models. arXiv 2016. Liu, Quan and Jiang, Hui and Evdokimov, Andrew and Ling, Zhen-Hua and Zhu, Xiaodan and Wei, Si and Hu, Yu. [Paper]
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. EACL 2017. Das, Rajarshi and Neelakantan, Arvind and Belanger, David and McCallum, Andrew. [Paper] [Code]
Differentiable learning of logical rules for knowledge base reasoning. NIPS 2017. Yang, Fan and Yang, Zhilin and Cohen, William W. [Paper]
Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. ICLR 2017. Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and Vilnis, Luke and Durugkar, Ishan and Krishnamurthy, Akshay and Smola, Alex and McCallum, Andrew. [Paper]
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. WWW 2019. Zhang, Wen and Paudel, Bibek and Wang, Liang and Chen, Jiaoyan and Zhu, Hai and Zhang, Wei and Bernstein, Abraham and Chen, Huajun. [Paper]
Variational reasoning for question answering with knowledge graph. AAAI 2018. Zhang, Yuyu and Dai, Hanjun and Kozareva, Zornitsa and Smola, Alexander J and Song, Le. [Paper]
The winograd schema challenge. Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning 2012. Levesque, Hector and Davis, Ernest and Morgenstern, Leora. [Paper]
Straight to the facts: Learning knowledge base retrieval for factual visual question answering. ECCV 2018. Narasimhan, Medhini and Schwing, Alexander G. [Paper]
Inferring and executing programs for visual reasoning. ICCV 2017. Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens and Hoffman, Judy and Fei-Fei, Li and Lawrence Zitnick, C and Girshick, Ross. [Paper]
End-to-end differentiable proving. NIPS 2017. Rocktäschel, Tim and Riedel, Sebastian. [Paper]
One-Shot Relational Learning for Knowledge Graphs. EMNLP 2018. Xiong, Wenhan, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. [Paper] [Code] [Note]
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs. CVPR 2018. Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang. [Paper]
Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. Kampffmeyer, Michael and Chen, Yinbo and Liang, Xiaodan and Wang, Hao and Zhang, Yujia and Xing, Eric P. [Paper] [Code]
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. CVPR 2018. Xiaolong Wang, Yufei Ye, Abhinav Gupta. [Paper] [Code]
Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification. AAAI 2019. Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun. [Paper] [Code]
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation. EMNLP 2018. Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong. [Paper]
Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge. ISWC 2019. Nikolaos Karalis, Georgios Mandilaras, and Manolis Koubarakis [Paper] [Code]
Revisiting the Representation of and Need for Raw Geometries on the Linked Data Web. CEUR Workshop Proceedings. [Paper]
Design and Development of Linked Data from The National Map. Semnantic Web Journal. E. Lynn Usery and Dalia Varanka [Paper]
Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model AGILE 2019. [Paper] [Code]
Enabling Spatio-Temporal Search in Open Data. Journal of Web Semantics (JWS), Elsevier, 2018. Sebastian Neumaier and Axel Polleres. [Paper]
Adaptive Low-level Storage of Very Large Knowledge Graphs. WWW 2020. Urbani et al.. [Paper]
- WordNet, https://wordnet.princeton.edu
- OpenCyc, https://www.cyc.com/opencyc/
- Cyc, https://www.cyc.com
- YAGO, http://www.mpii.mpg.de/∼suchanek/yago
- DBpedia, https://wiki.dbpedia.org/develop/datasets
- Freebase, https://developers.google.com/freebase/
- NELL, http://rtw.ml.cmu.edu/rtw/
- Wikidata, https://www.wikidata.org/wiki
- Probase IsA, https://concept.research.microsoft.com/Home/Download
- Google KG, https://developers.google.com/knowledge- graph
- A large-scale Chinese knowledge graph from OwnThink
- GDELT（Global Database of Events, Language, and Tone）Web
OpenKG knowledge graphs about the novel coronavirus COVID-19
COVID❋GRAPH COVID-19 virus [Web]
KgBase COVID-19 knowledge graph [Web] Academic graphs
- OAG, Open Academic Graph, https://www.aminer.cn/open-academic-graph
CORD-19, a comprehensieve named entity annotation dataset, CORD-NER, on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus [Data]
Baidu BROAD datasets [Web]
ASER: A Large-scale Eventuality Knowledge Graph WWW 2020. Zhang et al. [Paper]
Grakn, Grakn Knowledge Graph Library (ML R&D) https://grakn.ai
AmpliGraph, Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
OpenKE, An Open-Source Package for Knowledge Embedding (KE)
Fast-TransX, An Efficient implementation of TransE and its extended models for Knowledge Representation Learning
scikit-kge, Python library to compute knowledge graph embeddings
OpenNRE, An Open-Source Package for Neural Relation Extraction (NRE)
akutan, A distributed knowledge graph store
- OpenNRE, https://github.com/thunlp/OpenNRE
Knowledge graph APP, Simple knowledge graph applications can be easily built using JSON data managed entirely via a GraphQL layer. [Github] [Website]
- Stanford CS 520 Knowledge Graphs: How should AI explicitly represent knowledge? Vinay K. Chaudhri, Naren Chittar, Michael Genesereth. [Web]
- Stanford CS 224W: Machine Learning with Graphs. Jure Leskovec. [Web]
- University of Bonn: Analysis of Knowledge Graphs. Jens Lehmmann. [Web] [GitHub]
A repo about knowledge graph in Chinese - husthuke/awesome-knowledge-graph
A repo about NLP, KG, Dialogue Systems in Chinese - lihanghang/NLP-Knowledge-Graph
Top-level Conference Publications on Knowledge Graph - wds-seu/Knowledge-Graph-Publications
Geospatial Knowledge Graphs - semantic-geospatial
Acknoledgements give to the following people who comment or contribute to this repository (listed chronologically).