Github项目推荐 | 知识图谱文献集合

2019 年 4 月 12 日 AI研习社
Github项目推荐 | 知识图谱文献集合


本项目包含知识图谱的论文、代码和阅读笔记的集合。

by shaoxiongji


Github项目地址:

https://github.com/shaoxiongji/awesome-knowledge-graph 

注:本文的论文链接请点击底部【阅读原文】跳转查看

知识图谱嵌入

  • 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]

  • Bootstrapping Entity Alignment with Knowledge Graph Embedding. IJCAI 2018. Zequn Sun, Wei Hu, Qingheng Zhang and Yuzhong Qu. [Paper] [Code] [Note]

  • Does William Shakespeare Really Write Hamlet? Knowledge Representation Learning with Confidence. AAAI 2018. Ruobing Xie, Zhiyuan Liu, Fen Lin, and Leyu Lin. [Paper] [Code]

  • 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]

  • Enhanced Network Embeddings via Exploiting Edge Labels. CIKM 2018. Chen, Haochen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, and Steven Skiena. [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]

  • On the equivalence of holographic and complex embeddings for link prediction. ACL 2017. Hayashi, Katsuhiko and Shimbo, Masashi. [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]

  • 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]

  • 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. WWW 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]

  • 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]

  • Composing relationships with translations. EMNLP 2015. García-Durán, Alberto and Bordes, Antoine and Usunier, Nicolas. [Paper] [Code]


知识图谱补全

  • Embedding Multimodal Relational Data for Knowledge Base Completion. EMNLP 2018. Pezeshkpour, Pouya, Liyan Chen, and Sameer Singh. [Paper] [Code] [Note]

  • 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]

  • Compositional Vector Space Models for Knowledge Base Completion. ACL-IJCNLP 2015. Neelakantan, Arvind and Roth, Benjamin and McCallum, Andrew. [Paper]


关系提取

  • 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]

  • 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]


推荐系统

  • 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] [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]


问答系统

  • 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]

  • Commonsense for Generative Multi-hop Question Answering Tasks. EMNLP 2018. Bauer, Lisa, Yicheng Wang, and Mohit Bansal. [Paper] [Code] [Note]

  • EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs. ISWC 2018. Dubey, Mohnish, Debayan Banerjee, Debanjan Chaudhuri, and Jens Lehmann. [Paper] [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] [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]


对话生成

  • Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI 2018. Zhou, Hao, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. [Paper] [Note]


软件工程

  • 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]


其他应用

  • 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]

  • 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]


动态知识图谱

  • HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP 2018. Dasgupta, Shib Sankar, Swayambhu Nath Ray, and Partha Talukdar. [Paper] [Code] [Note]


知识图谱推断

  • 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]

  • 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]


One/few-Shot 及 Zero-Shot

  • 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. 2018. 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]

点击阅读原文,查看本文更多内容

登录查看更多
23

相关内容

知识图谱(Knowledge Graph),在图书情报界称为知识域可视化或知识领域映射地图,是显示知识发展进程与结构关系的一系列各种不同的图形,用可视化技术描述知识资源及其载体,挖掘、分析、构建、绘制和显示知识及它们之间的相互联系。 知识图谱是通过将应用数学、图形学、信息可视化技术、信息科学等学科的理论与方法与计量学引文分析、共现分析等方法结合,并利用可视化的图谱形象地展示学科的核心结构、发展历史、前沿领域以及整体知识架构达到多学科融合目的的现代理论。它能为学科研究提供切实的、有价值的参考。

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等
小贴士
相关资讯
17篇必看[知识图谱Knowledge Graphs] 论文@AAAI2020
Github项目推荐 | 全景分割相关资源列表
AI研习社
7+阅读 · 2019年5月13日
动态知识图谱补全论文合集
专知
35+阅读 · 2019年4月18日
Github项目推荐 | 图神经网络(GNN)相关资源大列表
【荟萃】知识图谱论文与笔记
专知
36+阅读 · 2019年3月25日
Github项目推荐 | awesome-bert:BERT相关资源大列表
AI研习社
27+阅读 · 2019年2月26日
知识表示学习领域代表论文全盘点
AI科技评论
6+阅读 · 2018年2月14日
相关VIP内容
专知会员服务
32+阅读 · 2020年3月19日
专知会员服务
81+阅读 · 2020年3月12日
专知会员服务
113+阅读 · 2020年2月13日
AAAI2020接受论文列表,1591篇论文目录全集
专知会员服务
73+阅读 · 2020年1月12日
计算机视觉最佳实践、代码示例和相关文档
专知会员服务
7+阅读 · 2019年10月9日
机器学习相关资源(框架、库、软件)大列表
专知会员服务
14+阅读 · 2019年10月9日
最新BERT相关论文清单,BERT-related Papers
专知会员服务
28+阅读 · 2019年9月29日
相关论文
Shaoxiong Ji,Shirui Pan,Erik Cambria,Pekka Marttinen,Philip S. Yu
72+阅读 · 2020年2月2日
Michael Azmy,Peng Shi,Jimmy Lin,Ihab F. Ilyas
3+阅读 · 2019年3月15日
Embedding Logical Queries on Knowledge Graphs
William L. Hamilton,Payal Bajaj,Marinka Zitnik,Dan Jurafsky,Jure Leskovec
3+阅读 · 2019年2月19日
Yixin Cao,Xiang Wang,Xiangnan He,Zikun hu,Tat-Seng Chua
5+阅读 · 2019年2月17日
Knowledge Representation Learning: A Quantitative Review
Yankai Lin,Xu Han,Ruobing Xie,Zhiyuan Liu,Maosong Sun
25+阅读 · 2018年12月28日
Haoyu Wang,Vivek Kulkarni,William Yang Wang
5+阅读 · 2018年10月31日
Agustinus Kristiadi,Mohammad Asif Khan,Denis Lukovnikov,Jens Lehmann,Asja Fischer
7+阅读 · 2018年5月25日
Tommaso Soru,Stefano Ruberto,Diego Moussallem,Edgard Marx,Diego Esteves,Axel-Cyrille Ngonga Ngomo
7+阅读 · 2018年3月21日
Liwei Cai,William Yang Wang
5+阅读 · 2018年2月20日
Top