A Survey on Application of Knowledge Graph

The papers mentioned in paper A Survey on Application of Knowledge Graph. You can find PDF files here.

Knowledge Bases

  1. DBpedia

    DBpedia-A crystallization point for the Web of Data. Christian Bizer, et al. Web Semantics: Science, Services and Agents on the World Wide Web 2009. [Paper]

  2. YAGO

    Yago: A large ontology from wikipedia and wordnet. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. Web Semantics: Science, Services and Agents on the World Wide Web 2008. [Paper]

  3. Wikidata [Website]

  4. Freebase

    Freebase: A shared database of structured general human knowledge. Kurt Bollacker, Robert Cook, and Patrick Tufts. AAAI 2007. [Paper]

Construction Techniques

  1. Knowledge graph construction techniques. (Chinese) Qiao Liu, Yang Li, Hong Duan, Yao Liu, Zhiguang Qing. Journal of Computer Research and Development (计算机研究与发展) 2016. [Paper]

  2. Review on knowledge graph techniques. (Chinese) Zenglin Xu, Yongpan Sheng, Lirong He, Yafang Wang. Journal of University of Electronic Science and Technology of China (电子科技大学学报) 2016. [Paper]

  3. Architecture of Knowledge Graph Construction Technique. Zhao, Zhanfang, Sung-Kook Han, and In-Mi So. International Journal of Pure and Applied Mathematics 2018. [Paper]


Question Answering Systems


  1. WebQuestion

    Semantic parsing on freebase from question-answer pairs. Jonathan Berant, et al. EMNLP 2013. [Paper]

  2. SimpleQuestion

    Large-scale simple question answering with memory networks. Antoine Bordes, et al. arXiv 2015. [Paper]


  1. Building Watson: An overview of the DeepQA project. David Ferrucci, Eric Brown, Jennifer Chu-Carroll, et al. AI Magazine 2010. [Paper]

  2. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. Li Zhou, Jianfeng Gao, Di Li, Heung-Yeung Shum. arXiv 2018. [Paper]

Semantic Parsing Based

  1. Semantic parsing on freebase from question-answer pairs. Jonathan Berant, Andrew Chou, Roy Frostig Percy Liang. EMNLP 2013. [Paper]
  2. Open question answering over curated and extracted knowledge bases. Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. KDD 2014. [Paper] [Code]

Information Retrievaling Based

  1. Information extraction over structured data: Question answering with freebase. Xuchen Yao, and Benjamin Van Durme. ACl 2014. [Paper]

Embedding Based

  1. Question answering with subgraph embeddings. Antoine Bordes, Sumit Chopra, and Jason Weston. EMNLP 2014. [Paper]

  2. Joint relational embeddings for knowledge-based question answering. Min-Chul Yang, et al. EMNLP 2014. [Paper]

  3. Open question answering with weakly supervised embedding models. Antoine Bordes, Jason Weston, and Nicolas Usunier. ECML PKDD 2014. [Paper]

Deep Learning

  1. Question answering over freebase with multi-column convolutional neural networks. Li Dong, et al. IJCNLP 2015. [Paper]

  2. An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. Yanchao Hao, et al. ACL 2017. [Paper]

  3. Semantic parsing via staged query graph generation: Question answering with knowledge base. Scott Wen-tau Yih, et al. IJCNLP 2015. [Paper]

  4. Question answering over knowledge base with neural attention combining global knowledge information. Yuanzhe Zhang, et al. ACL 2016. [Paper]

  5. A knowledge-grounded neural conversation model. Marjan Ghazvininejad, et al. AAAI 2018. [Paper]

  6. Key-value memory networks for directly reading documents. Alexander Miller, et al. ACL 2016. [Paper]

More Complex QA Tasks

  1. Commonsense for Generative Multi-Hop Question Answering Tasks. Lisa Bauer, Yicheng Wang, and Mohit Bansal. EMNLP 2018. [Paper] [Code]
  2. TEQUILA: Temporal Question Answering over Knowledge Bases. Zhen Jia, et al. CIKM 2018. [Paper]
  3. Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus. Iulian Vlad Serban, et al. ACL 2016. [Paper]
  4. Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base. Vishal Gupta, Manoj Chinnakotla, and Manish Shrivastava. CALCS 2018. [Paper]
  5. KG^ 2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings. Yuyu Zhang, et al. arXiv 2018. [Paper]

Recommender Systems

Embedding Based

  1. DKN: Deep knowledge-aware network for news recommendation. Hongwei Wang, et al. WWW 2018. [Paper]

  2. Collaborative knowledge base embedding for recommender systems. Fuzheng Zhang, et al. KDD 2016. [Paper]

  3. Shine: Signed heterogeneous information network embedding for sentiment link prediction. Hongwei Wang, et al. WSDM 2018. [Paper]

  4. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. Hongwei Wang, et al. WWW 2019. [Paper] [Code]

  5. Auto-encoding user ratings via knowledge graphs in recommendation scenarios. Vito Bellini, et al. DLRS 2017. [Paper]

Path Based

  1. Personalized entity recommendation: A heterogeneous information network approach. Xiao Yu, et al. WSDM 2014. [Paper]

  2. Meta-graph based recommendation fusion over heterogeneous information networks. Huan Zhao, et al. KDD 2017. [Paper]

  3. Explainable Reasoning over Knowledge Graphs for Recommendation. Xiang Wang, et al. AAAI 2019. [Paper] [Code]


  1. RippleNet: Propagating user preferences on the knowledge graph for recommender systems. Hongwei Wang, et al. CIKM 2018. [Paper] [Code]
  2. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. WWW 2019. [Paper]

Information Retrieval

  1. Entity query feature expansion using knowledge base links. Jeffrey Dalton, Laura Dietz, and James Allan. SIGIR 2014. [Paper]

  2. Latent entity space: a novel retrieval approach for entity-bearing queries. Xitong Liu, and Hui Fang. Information Retrieval Journal 2015. [Paper]

  3. Esdrank: Connecting query and documents through external semi-structured data. Chenyan Xiong, and Jamie Callan. CIKM 2015. [Paper]

  4. Word-entity duet representations for document ranking. Chenyan Xiong, Jamie Callan, and Tie-Yan Liu. SIGIR 2017. [Paper]

  5. Bag-of-Entities representation for ranking. Chenyan Xiong, Jamie Callan, and Tie-Yan Liu. ICTIR 2016. [Paper]

  6. Document retrieval using entity-based language models. Hadas Raviv, Oren Kurland, and David Carmel. SIGIR 2016. [Paper]

  7. Document retrieval model through semantic linking. Faezeh Ensan, and Ebrahim Bagheri. WSDM 2017. [Paper]

  8. Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval. Zhenghao Liu, et al. ACL 2018. [Paper] [Code]

  9. Explicit semantic ranking for academic search via knowledge graph embedding. Chenyan Xiong, Russell Power, and Jamie Callan. WWW 2017. [Paper]



  1. Knowlife: a knowledge graph for health and life sciences. Patrick Ernst, et al. ICDE 2014. [Paper]
  2. Semantic health knowledge graph: Semantic integration of heterogeneous medical knowledge and services. Longxiang Shi, et al. BioMed research international 2017. [Paper]
  3. Automatic generation of a qualified medical knowledge graph and its usage for retrieving patient cohorts from electronic medical records. Travis Goodwin, and Sanda M. Harabagiu. ICSC 2013. [Paper]
  4. Learning a health knowledge graph from electronic medical records. Maya Rotmensch, et al. Scientific Reports 2017. [Paper]

Cyber Security

  1. A Practical Approach to Constructing a Knowledge Graph for Cybersecurity. Yan Jia, et al. Engineering 2018. [Paper]
  2. Developing an Ontology for Cyber Security Knowledge Graphs. Michael D Iannacone, et al. CISR 2015. [Paper]
  3. Association Analysis Algorithm Based on Knowledge Graph for SPACE-Ground Integrated Network. Yulu Qi, et al. ICCT 2018. [Paper]
  4. ISEK: An information security knowledge graph for CISP knowledge system. Yuangang Yao, et al. ICITCS 2015. [Paper]
  5. Powering filtration process of cyber security ecosystem using knowledge graph. Claude Asamoah, et al. CSCloud 2016. [Paper]


  1. Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Volatility Prediction. Jue Liu, Zhuocheng Lu, and Wei Du. HICSS 2019. [Paper]
  2. Cyber incident classifications using ontology-based knowledge representation for cybersecurity insurance in financial industry. Sam Adam Elnagdy, Meikang Qiu, and Keke Gai. CSCloud 2016. [Paper]
  3. Understanding taxonomy of cyber risks for cybersecurity insurance of financial industry in cloud computing. Sam Adam Elnagdy, Meikang Qiu, and Keke Gai. CSCloud 2016. [Paper]
  4. Constructing Knowledge Graphs with Trust. Brian Ulicny. METHOD 2015. [Paper]


  1. DKN: Deep knowledge-aware network for news recommendation. Hongwei Wang, et al. WWW 2018. [Paper]
  2. Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata. Charlotte Rudnik, et al. Wiki Workshop 2019. [Paper]
  3. Fact checking in heterogeneous information networks. Baoxu Shi, and Tim Weninger. WWW 2016. [Paper]
  4. Building event-centric knowledge graphs from news. Marco Rospocher, et al. Journal of Web Semantics 2016. [Paper]
  5. Computational fact checking from knowledge networks. Giovanni Luca Ciampaglia, et al. PLoS ONE 2015. [Paper]
  6. Fake News Detection on Social Media: A Data Mining Perspective. Kai Shu, et al. SIGKDD 2017. [Paper]


  1. Information extraction and knowledge graph construction from geoscience literature. Chengbin Wang, et al. Computers & Geosciences 2018. [Paper]

  2. Intelligent learning for knowledge graph towards geological data. Yueqin Zhu, et al. Scientific Programming 2017. [Paper]


  1. KnowEdu: A System to Construct Knowledge Graph for Education. Penghe Chen, et al. IEEE Access 2018. [Paper]
  2. Knowledge graph-based teacher support for learning material authoring. Christian Grévisse, et al. CCC 2018. [Paper]
  3. Visualization for Knowledge Graph Based on Education Data. Kai Sun, et al. IJSI 2016. [Paper]

Other Applications

Social Network

  1. Social network de-anonymization and privacy inference with knowledge graph model. Jianwei Qian, et al. IEEE TDSC 2017. [Paper]
  2. De-anonymizing Social Networks and Inferring Private Attributes Using Knowledge Graphs. Jianwei Qian, et al. IEEE INFOCOM 2016. [Paper]


  1. Sentiment Analysis

    Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. Yukun Ma, Haiyun Peng, and Erik Cambria. AAAI 2018. [Paper]

  2. Image Classification

    Knowledge Graph-Based Image Classification Refinement. Dehai Zhang, et al. IEEE Access 2019. [Paper]

  3. Text Classification

    Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification. Jin Wang, et al. IJCAI 2017. [Paper]

Word Embedding

  • RC-NET: A General Framework for Incorporating Knowledge into Word Representations. Chang Xu, et al. CIKM 2014. [Paper]

Combating Human Trafficking

  • Building and using a knowledge graph to combat human trafficking. Pedro Szekely, et al. ISWC 2015. [Paper]