Dr. Jun Liu

              
    
       E-mail: liukeen(at)xjtu.edu.cn

 

Honors and Awards

2022, Excellent Student Paper Award (APWeb-WAIM 2022)
2021, Best Resource Paper Award (CCKS 2021)
2019, Wang Kuancheng Education Award
2018, Best Paper Award (NASAC 2018)
2018, Best Paper Award (ICBK 2018)
2018, Best Student Paper Award Candidate (ISWC 2018)
2016, Best Paper Award (The 27th ISSRE)
2014, IEEE Outstanding Service Award (The 14th IEEE CIT)
2013, Google Faculty Award
2010, IBM-CHINA Outstanding Teacher Award
2010, Young Scientist Award of ShaanXi Province
2009, National Second-class Award for Teaching
2008, New Century Excellent Talent of the Education Ministry of China
2006, Second Class Award for National Progress in Science and Technology
2004, Second Class Award of Progress in Science and Technology of the Education Ministry of China
2004, First Class Award of Progress in Science and Technology of shanghai City
2003, First Class Award of Progress in Science and Technology of Shaanxi Province
2003, “Hu’s” Scholarship in XJTU

Positions

Professor
Department of Computer Science
School of Electronic and Information Engineering
Xi’an Jiaotong University (XJTU), Xi’an 710049, China

Director
Shaanxi Province Key Lab of  Big Data Knowledge Engineering;

Xi'an Jiaotong University - Lenovo Smart Industries Joint Lab;
Institute of Multimedia Knowledge Fusion and Engineering.

Associate Editor: IEEE TNNLS (2020 ~)
Guest Editor: Information Fusion, ACM TOMM, IEEE Systems Journal, Future Generation Computer Systems     
IEEE Senior Member, CCF Senior Member

Education and Work Experiences

  • 12/2011–Present   Professor, Department of Computer Science and Technology, XJTU, China
  • 7/2019–8/2019     Senior Visiting Fellow, Karlsruhe Institute of Technology (KIT), Germany
  • 8/2017–9/2017     Senior Visiting Fellow, Stanford University, USA
  • 7/2016–8/2016     Senior Visiting Fellow, Queensland University, Australia
  • 7/2011–8/2011     Visiting Scholar, Iowa State University, USA
  • 7/1998–12/2011   Lecturer, Assoc. Prof., Department of Computer Science, XJTU, China
  • 5/2005–8/2005     Visiting Scholar, University of Hong Kong, Hong Kong, China
  • 9/1999–3/2004     Ph.D., Systems Engineering, XJTU, China
  • 9/1995–7/1998     M.Eng., Computer Architecture, XJTU, China 
  • 9/1991–7/1995     B.Eng., Computer Science and Technology, Xi’an Jiaotong University (XJTU), China

Research Projects

  • 1/2023–12/2027   Cross-media teaching resources understanding and personalized navigation learning, funded by the National Natural Science Foundation of China (62293553)
  • 1/2023–12/2023   Visual Perception Principles induced Diagram understanding, funded by the National Natural Science Foundation of China (62250066)
  • 10/2022–9/2025   Aggregation and Guided Learning of Cross-Media Teaching Resources, funded by the National Key Research and Development Program of China (2022YFC3303603)
  • 1/2022–12/2025   Representation Learning and Differentiable Reasoning of First-order Logic Formulas, funded by the National Natural Science Foundation of China (62176207)
  • 1/2020–10/2020   Intelligent Tutoring Systems based on Knowledge Forest, funded by Lenovo Research
  • 5/2018–4/2021     Educational Data Analysis and Mining, funded by the National Key Research and Development Program of China (2018YFB1004500)
  • 1/2017–12/2020   Faceted Fusion of Knowledge Fragments from Open Knowledge Sources, funded by the National Natural Science Foundation of China (61672419)
  • 1/2016–12/2020   Massive Online Collaborative Learning, funded by the National Natural Science Foundation of China (61532004)
  • 1/2012–12/2014   Content Management and Analysis of Massive Web Data, funded by the Hi-Tech R&D (National 863) Project of China (2012AA011003)
  • 7/2008–7/2010     Knowledge Discovery and Value-added Service in the field of Education, funded by the Hi-Tech R&D (National 863) Project of China (2008AA01Z131)
  • 1/2009–12/2011   Mining Knowledge Elements and their Association from Domain-specific Text, funded by the National Natural Science Foundation of China (60803079)
  • 1/2012–12/2015   Topology and evolution characteristics of knowledge map, funded by the National Natural Science Foundation of China (61173112)


Research Interests: NLP, CV, e-learning

Selected Publications

2023:

       Journal  Papers

  • Fangzhi Xu, Jun Liu, Qika Lin, Tianzhe Zhao, Jian Zhang, Lingling Zhang, Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text, IEEE TNNLS, 2023, Accepted.
  • Changyu Wang, Pinghui Wang, Tao Qin, Chenxu Wang, Suhansanu Kumar, Xiaohong Guan, Jun Liu, and Kevin Chen-ChuanChang. SocialSift: Target Query Discovery on Online Social Media With Deep Reinforcement Learning, IEEE TNNLS, 2023.
  • Yaxian Wang, Bifan Wei, Jun Liu, Lingling Zhang, Jiaxin Wang, Qianying Wang, DisAVR: Disentangled Adaptive Visual Reasoning Network for Diagram Question Answering, IEEE TIP, 2023, Accepted.
  • Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qing-Hu Zheng. XTQA: Span-Level Explanations for Textbook Question Answering, IEEE TNNLS, 2023, Accepted.
  • Yudai Pan, Jun Liu, Lingling Zhang, Yi Huang. Incorporating logic rules with textual representations for interpretable knowledge graph reasoning, Knowledge-Based Systems, 2023, Accepted.
  • Fangzhi Xu, Qika Lin, Jun Liu, Lingling Zhang, Tianzhe Zhao, Qi Chai, Yudai Pan, Yi Huang, Qianying Wang. MoCA: Incorporating Domain Pretraining and Cross Attention for Textbook Question Answering, Pattern Recognition, 2023(140): 109588.
  • Yaxian Wang, Jun Liu, Ma Jie, Hongwei Zeng, Lingling Zhang, Junjun Li. Dynamic Dual Graph Networks for Textbook Question Answering, Pattern Recognition, 2023, Accepted.
  • Lingling Zhang, Xinyu Zhang, Qianying Wang, Wenjun Wu, Xiaojun Chang, Jun Liu. RPMG-FSS: Robust Prior Mask Guided Few-Shot Semantic Segmentation, IEEE TCSVT, 2023, Accepted.
  • 郑庆华, 刘欢, 龚铁梁, 张玲玲, 刘均. 大数据知识工程发展现状与未来展望, 中国工程科学, 2023, 已录用.
  • Bin Shang, Yinliang Zhao, Jun Liu, Yifan Liu, Chenxin Wang. A Contrastive Knowledge Graph Completion Model with Hierarchical Attention and Dynamic Completion, Neural Computing and Applications, 2023, Accepted.
  • Lingyun Song, Mengting He, Xuequn Shang, Chen Yang, Jun Liu, Mengzhen Yu, Yu Lu. A Deep Cross-modal Neural Cognitive Diagnosis Framework for Modeling Student Performance, Expert Systems With Applications, 2023, Accepted.

     Conference Papers

  • Yuecheng Rong, Juntao Yao, Jun Liu, Yifan Fang, Wei Luo, Hao Liu, Jie Ma, Zepeng Dan, Jinzhu Lin, Zhi Wu, Yan Zhang, Chuanming Zhang. GBTTE: Graph Attention Network Based Bus Travel Time Estimation. CIKM 2023
  • Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, Jin Song Dong. B2-Sampling: Fusing Balanced and Biased Sampling for Graph Contrastive Learning,  KDD 2023.
  • Bin Shang, Yinliang Zhao, Di Wang, Jun Liu. Relation-Aware Multi-Positive Contrastive Knowledge Graph Completion with Embedding Dimension Scaling. SIGIR 2023.
  • Xin Hu, Lingling Zhang, Jun Liu, Jinfu Fan,  Yang You, Yaqiang Wu. GPTR: Gestalt-Perception Transformer for Diagram Object Detection, AAAI 2023.
  • Xin Hu, Lingling Zhang, Jun Liu, Yang You, Yaqiang Wu. Where to see: Gestalt-perceptual Attention for Diagram Visual Grounding, IJCAI 2023.
  • Hongwei Zeng, Jun Liu, Bifan Wei and Weiping Fu. Synthesize, Prompt and Transfer: Zero-shot Conversational Question Generation with Pre-trained Language Model, ACL 2023.
  • Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu and Erik Cambria. TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning, ACL 2023.

2022:

       Journal  Papers

  • Yaxian Wang, Bifan Wei, Jun Liu, Qika Lin, Lingling Zhang, Yaqiang  Wu, Spatial-Semantic Collaborative Graph Network for Textbook Question Answering, IEEE TCSVT, 2022, Accepted.
  • Jiaxin Wang, Lingling Zhang, Jun Liu, Kunming Ma, Wenjun Wu, Xiang Zhao, Yaqiang Wu, Yi Huang. TGIN: Translation-Based Graph Inference Network for Few-Shot Relational Triplet Extraction. IEEE TNNLS, 2022, Accepted.
  • Qika Lin, Rui Mao, Jun Liu, Fangzhi Xu, Erik Cambria. Fusing Topology Contexts and Logical Rules in Language Models for Knowledge Graph Completion. Information Fusion, 2022, Accepted.
  • Yuecheng Rong, Zhimian Xu, Jun Liu, Hao Liu, Jian Ding, Xuanyu Liu, Wei Luo, Chuanming Zhang and Jiaxiang Gao, Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-source Data, IEEE TITS, 2022, Accepted.
  • Song Lingyun, Li Jiaoao, Liu Jun, Yang Yang, Shang Xuequn, Sun Mingxuan. Answering Knowledge-based Visual Questions via the Exploration of Question Purpose. Pattern Recognition, 2022, Accepted.
  • Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Zhihui Li, Lina Yao, and Alex Hauptmann, TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection, IEEE TPAMI, 2022, Accepted.
  • Jie Ma, Qi Chai, Jingyue Huang, Jun Liu, Yang You, Qinghua Zheng, Weakly Supervised Learning for Textbook Question Answering, IEEE TIP, 2022, Accepted.
  • Jianming Zheng, Fei Cai, Jun Liu, Yanxiang Ling, and Honghui Chen, Multistructure Contrastive Learning for Event Representation, IEEE TNNLS, 2022, Accepted.
  • Lingling Zhang, Shaowei Wang, Jun Liu, Xiaojun Chang, Qika Lin, Yaqiang Wu, and Qinghua Zheng, MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification，IEEE TKDE, 2022, Accepted.
  • Yanxiang Ling, Fei Cai, Jun Liu, Honghui Chen and Maarten de Rijke, Generating Relevant and Informative Questions for Open-domain Conversations, ACM TOIS, 2022, Accepted. 
  • Shaowei Wang, Linging Zhang, Xuan Luo, Yi Yang, Xin Hu, Tao Qin, Jun Liu, Computer Science Diagram Understanding with Topology Parsing, ACM TKDD, 2022, Accepted. 
  • Lingyun Song, Mengzhen Yu, Xuequn Shang, Yu Lu, Jun Liu, Ying Zhang, Zhanhuai Li, A deep grouping fusion neural network for multimedia content understanding, IET Image Process, 2022, Accepted.

     Conference Papers

  • Yudai Pan, Jun Liu, Lingling Zhang, Tianzhe Zhao, Qika Lin, Xin Hu and Qianying Wang. Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations. EMNLP 2022.
  • Jiaxin Wang, Lingling Zhang, Jun Liu, Liang Xi, Yujie Zhong and Yaqiang Wu. MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering. EMNLP 2022.
  • Yuecheng Rong, Jun Liu, Zhilin Xu, Jian Ding, Chuanming Zhang, Jiaxiang Gao. BusWTE: Realtime Bus Waiting Time Estimation of GPS Missing via Multi-Task Learning. ECML-PKDD 2022.
  • Siyu Yao, Tianzhe Zhao, Fangzhi Xu, Jun Liu. Incorporating prior type information for few-shot knowledge graph completion. APWeb-WAIM 2022. (Excellent Student Paper Award)
  • Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan and Lingling Zhang. Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning, SIGIR 2022.
  • Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang and Tianzhe Zhao. Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction, SIGIR 2022.

 

2021:

     Book/Book Chapter

  • 郑庆华、刘均、魏笔凡、张玲玲. 知识森林：理论、方法与实践, 科学出版社, 2021.
  • Jun Liu, Lingling Zhang, Bifan Wei, Qinghua Zheng. Virtual Teaching Assistants: Technologies, Applications and Challenges. In: Fang Chen, Jianlong Zhou (Eds.), Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership, Springer, 2021.

       Journal  Papers

  • Qika Lin; Jun Liu, Lingling Zhang, Yudai Pan, Xin Hu, Fangzhi Xu, Hongwei Zeng, Contrastive Graph Representations for Logical Formulas Embedding, IEEE TKDE, 2021, Accepted.
  • 张玲玲, 陈一苇, 吴文俊, 魏笔凡, 罗炫, 常晓军, 刘均. 基于对比约束的可解释小样本学习. 计算机研究与发展, 2021, 58(12): 2573-2584.
  • 蔺奇卡, 张玲玲, 刘均,赵天哲. 基于问句感知图卷积的教育知识库问答方法. 计算机科学与探索, 2021, 15(10): 1880-1887
  • Yanxiang Ling, Fei Cai, Jun Liu, Honghui Chen, and Maarten de Rijke, Keep and Select: Improving Hierarchical Context Modeling for Multi-turn Response Generation, IEEE TNNLS, 2021, Accepted.
  • Xin Hu, Lingling Zhang, Jun Liu, Qinghua Zheng, Jianlong Zhou, Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model, IEEE TIP, 2021, Accepted.
  • Jie Ma, Jun Liu, Qika Lin; Bei Wu; Yaxian Wang; Yang You. Multi-Task Learning for Visual Question Answering, IEEE TNNLS, 2021, Accepted.
  • Qika Lin, Jun Liu, Yudai Pan, Lingling Zhang, Xin Hu, Jie Ma. Rule-Enhanced Iterative Complementation for Knowledge Graph Reasoning, Information Sciences, 2021, Accepted.
  • Jie Ma, Jun Liu, Yaxian Wang, Junjun Li, and Tongliang Liu. Relation-aware Fine-grained Reasoning Network for Textbook Question Answering, IEEE TNNLS, 2021, Accepted.
  • Lingling Zhang, Shaowei Wang, Xiaojun Chang, Jun Liu, Zongyuan Ge, and Qinghua Zheng. Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning, IEEE TCSVT, 2021, Accepted.
  • Hongwei Zeng, Zhuo Zhi, Jun Liu, Bifan Wei. Improving Paragraph-level Question Generation with Extended Answer Network and Uncertainty-aware Beam Search. Information Sciences, 2021, Accepted.
  • Hongwei Zeng, Jun Liu, Meng Wang, Bifan Wei. A Sequence to Sequence Model for Dialogue Generation with Gated Mixture of Topics. Neurocomputing, 2021, Accepted.
  • Yanxiang Ling, Fei Cai, Xuejun Hu, Jun Liu, Wanyu Chen, and Honghui Chen. Context-Controlled Topic-Aware Neural Response Generation for Open-Domain Dialog Systems. Information Processing & Management, 2021, 58(1): 102392.

     Conference Papers

  • Shaowei Wang, Lingling Zhang, Yi Yang, Xin Hu, Tao Qin, Bifan Wei, Jun Liu. CSDQA: Diagram Question Answering in Computer Science, CCKS 2021. （Best Resource Paper Award）
  • Yanzhang Lyu, Hongzhi Yin, Jun Liu, Mengyue Liu, Huan Liu, Shizhuo Deng. Reliable Recommendation with Review-level Explanations. ICDE 2021.
  • Wenjun Wu, Lingling Zhang, Yiwei Chen, Xuan Luo, Bifan Wei, and Jun Liu. Fuzzy c-Means Clustering with Discriminative Projection, ICBK 2021.
  • Hongwei Zeng, Zhenjie Hong, Jun Liu, and Bifan Wei. Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling, ICBK 2021.
  • Hongxuan Li, Bifan Wei, Jun Liu, Zhaotong Guo, Jingchao Qi, Yong Liu, and Yuanyuan Shi. ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters, ICBK 2021.

 

2020:

       Book/Book Chapter

  • Siyu Yao, Ruijie Wang, Shen Sun, Derui Bu, Jun Liu. Joint Embedding Learning of Educational Knowledge Graphs. In: Pinkwart N., Liu S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, 2020.

     Journal  Papers

  • Lingyun Song, Jun Liu, Mingxuan Sun, Xuequn Shang. Weakly Supervised Group Mask Network for Object Detection. IJCV, 2020, Accepted.
  • 麻珂欣, 魏笔凡, 马杰, 刘均, 黄毅, 胡珉, 冯俊兰. 知识主题间先序关系挖掘, 大数据, 2020, 已录用.
  • 姚思雨,  赵天哲, 王瑞杰. 刘均. 规则引导的知识图谱联合嵌入方法, 计算机研究与发展, 2020, 已录用.
  • Bei Wu; Bifan Wei, Jun Liu, Kewei Wu, Meng Wang, Faceted Text Segmentation via Multi-Task Learning, IEEE TNNLS, 2020, Accepted.
  • Xin Hu, Jun Liu, Jie Ma, Yudai Pan, Lingling Zhang, Fine-grained 3D-Attention Prototypes for Few-Shot Learning, Neural Computation, 2020, 32(9): 1664-1684.
  • Chenxu Wang, Wei Rao, Wenna Guo, Pinghui Wang, Jun Liu, Xiaohong Guan,Towards Understanding the Instability of Network Embedding, IEEE TKDE,  2020, Accepted.
  • Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Mahesh Prakash, Alexander Hauptmann, Few-Shot Activity Recognition with Cross-Modal Memory Network, Pattern Recognition, 2020, 108: 107348.
  • 范铭,  刘烃, 刘均,  罗夏朴, 于乐, 管晓宏. 安卓恶意软件检测方法综述, 中国科学: 信息科学, 2020, 50(8): 1148-1177.

     Conference Papers

  • Lingling Zhang, Xiaojun Chang, Jun Liu, Sen Wang, Zongyuan Ge, Minnan Luo, Alexander Hauptmann, ZSTAD: Zero-Shot Temporal Activity Detection, CVPR 2020.

2019:

     Journal  Papers

  • Ruijie Wang; Meng Wang; Jun Liu; Michael Cochez. Structured Query Construction via Knowledge Graph Embedding, KAIS, 2019, Accepted. 
  • Lingling Zhang, Minnan Luo, Jun Liu, Xiaojun Chang, Yi Yang, and Alexander G. Hauptmann. Deep Top-k Ranking for Image-Sentence Matching, IEEE TMM, 2019, 22(3): 775-785.
  • Lingling Zhang, Jun Liu, Minnan Luo, Xiaojun Chang, Qinghua Zheng, Alexander G. Hauptmann. Scheduled Sampling for One-Shot Learning via Matching Network, Pattern Recognition, 2019, 96: 106962.
  • Qinghua Zheng, Jun Liu, Hongwei Zeng, Zhaotong Guo, Bei Wu & Bifan Wei. Knowledge Forest: A Novel Model to Organize Knowledge Fragments, Science China (Information Sciences), 2019, Accepted.
  • Zheng Yan, Jun Liu, Laurence T. Yang, Witold Pedrycz. [Editorial] Data fusion in heterogeneous networks, Information Fusion, 2020, 53, 1-3.
  • Ming Fan, Xiapu Luo, Jun Liu, Chunyin Nong, Qinghua Zheng, Ting Liu. CTDroid: Leveraging a Corpus of Technical Blogs for Android Malware Analysis, IEEE Transactions on Reliability, 2019, 69(1): 124-138.
  • Mengyue Liu, Jun Liu, Yihe Chen, Hao Chen, Meng Wang, Qinghua Zheng. AHNG: Representation Learning on Attributed Heterogeneous Network, Information Fusion, 2019, 50: 221-230.
  • 郑庆华，董博，钱步月，田锋，魏笔凡，张未展，刘均. 智慧教育研究现状与发展趋势, 计算机研究与发展, 2019, 56(1), 209-224.
  • Meng Wang, Jun Liu, Bifan Wei, Siyu Yao, Hongwei Zeng, Lei Shi. Answering Why-Not Questions on SPARQL Queries. KAIS. 2019, 58(1): 169-208.
  • Wenqiang Liu, Jun Liu, Bifan Wei, Yanan Qian, Haimeng Duan, Wei Hu, Xindong Wu. A New Truth Discovery Method for Resolving Object Conflicts over Linked Data with Scale-free Property. KAIS, 2019, 59(2): 465-495.

     Conference Papers

  • Jie Ma, Jun Liu, Yufei Li, Xin Hu, Yudai Pan, Shen Sun and Qika Lin. Jointly Optimized Neural Coreference Resolution with Mutual Attention. WSDM 2019.
  • Ruijie Wang , Meng Wang, Jun Liu, Michael Cochez, and Stefan Decker. Leveraging Knowledge Graph Embeddings for Natural Language Question Answering. DASFAA 2019. 
  • Zhaotong Guo, Bifan Wei, Jun Liu, Bei Wu. TF-Miner: Topic-specific Facet Mining by Label Propagation. DASFAA 2019. 
  • Luguo Xue, Minnan Luo, Zhen Peng, Jundong Li, Yan Chen, Jun Liu, Anomaly Detection in Time-Evolving Attributed Networks. DASFAA 2019.

 

2018:

     Journal  Papers

  • Wenqiang Liu, Jun Liu, Mengmeng Wu, Wei Hu, Bifan Wei, Qinghua Zheng. Representation Learning over Multiple Knowledge Graphs for Knowledge Graphs Alignment, Neurocomputing, 2018, 320: 12-24.
  • Lingyun Song, Jun Liu, Buyue Qian, Mingxuan Sun,et al. A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification. IEEE TIP, 2018, 27(12): 6025-6038.
  • Ming Fan, Jun Liu, Xiapu Luo, Kai Chen, Zhenzhou Tian, Qinghua Zheng, Ting Liu. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis.  IEEE TIFS, 2018, 13(8), 1890-1905 .
  • Bei Wu, Bifan Wei, Jun Liu, Zhaotong Guo, Yuanhao Zheng, Yihe Chen. Facet Annotation by Extending CNN with a Matching Strategy. Neural Computation. 2018, 30(6), 1647-1672.
  • Lingling Zhang, Jun Liu, Ninnan Luo, Xiaojun Chang, Qinghua Zheng. Deep Semi-supervised Zero-shot Learning with Maximum Mean Discrepancy. Neural Computation, 2018, 30(5), 1426-1447.
  • Lingling Zhang, Ninnan Luo, Zhihui Li, Feiping Nie, Huangxiang Zhang, Jun Liu, Qinghua Zheng. Large Scale Robust Semi-supervised Classification. IEEE Transactions on Cybernetics, 2018, 49(3): 907-917.
  • Zheng Yan, Jun Liu, Laurence T. Yang, Nitesh Chawla. [Editorial]  Big Data Fusion in Internet of Things, Information Fusion, 2018, 40, 32-33.
  • Hao Chen, Jun Liu, Yanzhang Lv, Max Haifei Li, Mengyue Liu. Semi-supervised Clues Fusion for Spammer Detection in Sina Weibo. Information Fusion. 2018, 44, 22-32.

     Conference Papers

  • Ming Fan, Xiapu Luo, Jun Liu, Meng Wang, Chunyin Nong, Qinghua Zheng and Ting Liu. Graph Embedding based Familial Analysis of Android Malware using Unsupervised Learning, ICSE 2019. 
  • Ming Fan, Xiapu Luo, Jun Liu, Chunyin Nong, Qinghua Zheng and Ting Liu, CTDroid: Leveraging a Corpus of Technical Blogs for Android Malware Analysis. NASAC 2018.  (Best Paper Award)
  • Lingyun Song, Jun Liu, Buyue Qian, Yihe Chen. Connecting Language to Images: A Progressive Attention-Guided Network for Simultaneous Image Captioning and Language Grounding, AAAI 2019. 
  • Ruijie Wang, Meng Wang, and Jun Liu. Graph Embedding based Query Construction over Knowledge Graphs. IEEE ICBK 2018. （Best Paper Award)
  • Ruoqing Ren, Haimeng Duan, Wenqiang Liu and Jun Liu. AUnet: An Unsupervised Method for Answer Reliability Evaluation in Community QA Systems, DMMOOC 2018.
  • Meng Wang, Ruijie Wang, Jun Liu, Yihe Chen, Lei Zhang, Guilin Qi. Towards Empty Answers in SPARQL: Approximating Querying with RDF Embedding, ISWC 2018. (Best Student Paper Award Candidate)
  • Yu Tong, Wang Meng, Lv Yanzhang, Xue Luguo and Liu Jun. Interpretative Topic Categorization via Deep Multiple Instance Learning, IJCNN 2018. 
  • Hao Chen, Jun Liu, Yanzhang Lv. A Transfer Metric Learning Method for Spammer Detection.PAKDD 2018.

 

2017: 

     Journal  Papers

  • Lei Ding, Jun Liu, Tao Qin, Haifei Li. Internet Traffic Classification Based on Expanding Vector of Flow. Computer Networks. 2017, 129, 178-192.
  • Meng Wang, Weitong Chen, Sen Wang, Jun Liu, Xue Li, Bela Stantic. Answering Why-Not Questions on Semantic Multimedia Queries, Multimedia Tools and Applications, 2017, 77(8), 1-25.
  • Lingyun Song, Jun Liu, Minnan Luo, Buyue Qian, Kuan Yang.Sparse Relational Topical Coding on Multi-Modal Data, Pattern Recognition, 2017, 72, 368-380.
  • Xindong Wu, Huanhuan Chen, Jun Liu, Gongqing Wu, Ruqian Lu, and Nanning Zheng. Knowledge Engineering with Big Data (BigKE): A 54-Month, 45-Million RMB, 15-Institution National Grand Project, IEEE Access, 2017, 5(99), 12696-12701.
  • Ming Fan, Jun Liu, Wei Wang, Haifei Li, Zhenzhou Tian, Ting Liu, DAPASA: Detecting Android Piggybacked Apps through Sensitive Subgraph Analysis, IEEE TIFS, 2017, 12(8), 1772-1785.
  • Yanzhang Lv, Jun Liu, Hao Chen, Jianhong Mi, Mengyue Liu and Qinghua Zheng, Opinioned Post Detection in Sina Weibo, IEEE Access, 2017, 5(1), 7263-7271.
  • Jun Liu, Zheng Yan, Athanasios V. Vasilakos, and Laurence T. Yang. [Editorial] Data Mining in Cyber, Physical and Social Computing, IEEE SYSTEMS JOURNAL, 2017, 11(1), 194-196
  • Minnan Luo, Lingling Zhang, Jun Liu and Qinghua Zheng, Distributed Extreme Learning Machine with Alternating Direction Method of Multiplier, Neurocomputing, 2017, 261, 164-170. 

     Conference Papers

  • Yuanhao Zheng, Bifan Wei, Jun Liu, Meng Wang, Weitong Chen, Bei Wu and Yihe Chen.Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning, ADMA 2017.
  • Meng Wang, Jiaheng Zhang, Jun Liu, Wei Hu, Sen Wang and Wenqiang Liu. PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking, ISWC 2017. 
  • Haimeng Duan, Yuanhao Zheng, Lei Shi, Changhong Jin, Hongwei Zeng,and Jun Liu, DKG: An Expanded Knowledge Base for Online Course, DMMOOC 2017.
  • Wenqiang Liu, Jun Liu, Haimeng Duan, Wei Hu and Bifan Wei, Exploiting Source-Object Network to Resolve Object Conflicts in Linked Data, ESWC 2017. 
  • Wenqiang Liu, Jun Liu, Haimeng Duan, Jian Zhang, Wei Hu, and Bifan Wei. [Demo]TruthDiscover: Resolving Object Conflicts on Massive Linked Data, WWW 2017.

 

2009~2016: 

     Journal  Papers

  • Wenqiang Liu, Jun Liu, Meng Wang, Qinghua Zheng, Wei Zhang, Lingyun Song, Siyu Yao. Faceted Fusion of RDF Data. Information Fusion, 2015, 23, 16-24
  • Weizhan Zhang, Jun Liu, Chen Liu, Qinghua Zheng, Wei Zhang. Workload Modeling for Virtual Machine-hosted Application. Expert Systems With Applications, 2015, 42(4): 1835-1844.
  • 吴信东, 陈欢欢, 刘均, 大数据知识工程基础理论及其应用研究, 中国计算机学会通讯, 2016, 12(11), 68-72
  • Lingyun Song, Minnan Luo, Jun Liu, Lingling Zhang, Haifei Li, Qinghua Zheng, Sparse Multi-Modal Topical Coding for Image Annotation, Neurocomputing, 2016, 214, 162-174.
  • Xindong Wu, Huanhuan Chen, Gong-Qing Wu, Jun Liu, Qinghua Zheng, Xiaofeng He, Aoying Zhou, Zhong-Qiu Zhao, Bifan Wei, Ming Gao, Yang Li, Qiping Zhang, Shichao Zhang, Nanning Zheng, Knowledge Engineering with Big Data, IEEE Intelligent Systems, 2015, 30(5), 46-55
  • Jun Liu, Zheng Yan, Laurance T. Yang. [Editorial] Fusion – An aide to data mining in Internet of Things. Information Fusion, 2015, 23, 1-2
  • Zheng Yan, Jun Liu, Athanasios Vasilakos, Laurance T. Yang, [Editorial] Trustworthy Data Fusion and Mining in Internet of Things. FGCS, 2015, 49, 45-46
  • Bifan Wei, Jun Liu, Qinghua Zheng, Wei Zhang, Chenchen Wang, Bei Wu, DF-Miner: Domain-specific Facet Mining by Leveraging the Hyperlink Structure of Wikipedia. Knowledge-Based Systems, 2015, 77, 80-91
  • Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng. Motif-based Hyponym Relation Extraction from Wikipedia Hyperlinks. IEEE TKDE, 2014, 26(10): 2507-2519.
  • Bifan Wei, Jun Liu, Qinghua Zheng, Wei Zhang, Xiaoyu Fu, Boqin Feng. A Survey of Faceted Search. Journal of Web Engineering, 2013,12(1-2):41-64.
  • Jun Liu, Jincheng Wang, Qinghua Zheng, Wei Zhang, Lu Jiang. Topological Analysis of Knowledge Maps. Knowledge-Based Systems, 2012, 36, 260-267.
  • Jun Liu,  Lu Jiang,  Zhaohui Wu,  Qinghua Zheng  and  Yanan Qian. Mining Learning-Dependency between Knowledge Units from Text. VLDB J.  2011, 20(3): 335-345
  • Jun Liu,  Lu Jiang,  Zhaohui Wu,  Qinghua Zheng. Deep Web Adaptive Crawling based on Minimum Executable Pattern. Journal of Intelligent Information Systems, 2011, 36(2): 197-215

     Conference Papers

  • Ming Fan, Jun Liu, Xiapu Luo, Kai Chen, Tianyi Chen, Zhenzhou Tian, Xiaodong Zhang and Ting Liu. Frequent Subgraph based Familial Classification of Android Malware. ISSRE2016. (Best Paper Award)
  • Siyu Yao, Jun Liu, Meng Wang, Bifan Wei and Xuelu Chen.  [Demo]ANNA: Answering Why-Not Questions for SPARQL, ISWC 2015.
  • Minnan Luo, Lingling Zhang, Qinghua Zheng, and Jun Liu. Distributed Extreme Learning Machine with Alternating Direction Method of Multiplier, ELM 2015.
  • Meng Wang, Jun Liu, Wenqiang Liu, Qinghua Zheng, Wei Zhang, Lingyun Song, Siyu Yao. Faceted Exploring for Domain Knowledge over Linked Open Data, CIKM2014.
  • Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng. DFT-extractor: A System to Extract Domain-specific Faceted Taxonomies from Wikipedia. WWW2013.
  • Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng. MOTIF-RE: Motif-based Hypernym/hyponym Relation Extraction from Wikipedia Links. ICONIP2012.
  • Jun Liu,  Lu Jiang, Zhaohui Wu, Qinghua Zheng. Mining Preorder Relation between Knowledge Units from Text. ACM SAC 2010.