刘均  (教授)

博士生导师 硕士生导师

入职时间：1998-06-01

学历：博士研究生毕业

性别：男

学位：博士

在职信息：在职

毕业院校：西安交通大学

学科：计算机科学与技术

   
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    • 2025.1 ~ 2026.12，国家自然科学基金原创探索计划项目(延续)，融合视觉感知准则与内在机理的示意图深度理解方法（62450005）.
    • 2023.7 ~ 2025.06，陕西省重点研发计划项目，虚实融合的智能教学环境构建技术及示范应用（2023GXLH-023）.
    • 2023.1 ~ 2027.12，国家自然科学基金重大项目课题，跨媒体教学资源理解与个性化导学方法研究（62293553）.
    • 2023.1 ~ 2023.12，国家自然科学基金原创探索计划项目，视觉感知准则引导的示意图理解方法（62250066）.
    • 2023.1 ~ 2024.10，西安交通大学本科教学改革研究项目（重点项目），基于采集式学习的实践教学模式探索.
    • 2022.1 ~ 2025.12，国家自然科学基金项目，一阶逻辑公式的表征学习与可微推理关键技术研究（62176207）.
    • 2020.1 ~ 2020.10，联想横向课题，基于知识森林的教与学系统.
    • 2020.1 ~ 2020.12，弘成科技合作协同育人项目，数据挖掘.
    • 2018.5 ~ 2021.4，国家重点研发计划“云计算和大数据”重点专项，教育大数据分析挖掘技术及其智慧教育示范应用（2018YFB1004500）.
    • 2018.1 ~ 2018.12，Intel公司产学合作协同育人项目，自然语言理解与机器翻译.
    • 2018.1 ~ 2019.12，西安交通大学在线教学改革研究专项项目，基于知识森林的碎片化知识组织与导航学习新模式.
    • 2017.1 ~ 2020.12，国家自然科学基金项目，面向开放知识源的知识碎片分面聚合方法研究（61672419）.
    • 2016.1 ~ 2017.12，教育部在线教育研究基金课题，基于大数据挖掘和分析的学习者学习路径优化研究.
    • 2016.1 ~ 2020.12，国家自然科学基金重点项目（子项目），大规模在线协同学习的机理与方法研究（61532004）.
    • 2014.1 ~ 2016.12，西安交通大学青年教师跟踪支持项目，知识地图导航学习理论与方法.
    • 2012.1 ~ 2014.12，国家“863”计划子课题，海量web数据内容管理、分析挖掘技术与大型示范应用（2012AA011003）.
    • 2011.1 ~ 2014.12，国家自然科学基金项目，知识地图的拓扑与演化特性研究及在e-Learning中的应用（61173112）.
    • 2010.1 ~ 2011.12，教育部专项课题，基于知识元的科技论文检索方法研究与应用.
    • 2010.1 ~ 2012.12，“核高基”国家科技重大专项分课题，基于国产基础软件的数字教育关键技术攻关及示范应用（2010ZX01045-001-005-2）.
    • 2009.1 ~ 2011.12，国家自然科学基金项目，面向特定领域文本的知识元及其关联挖掘方法研究（60803079）.
    • 2009.1 ~ 2011.12，教育部“新世纪优秀人才支持计划”项目，面向非结构文本的知识元关联挖掘方法研究（NCET-08-0433）.
    • 2008.7 ~ 2010.6，国家“863”计划目标导向课题，面向教育的海量知识资源组织、管理与服务系统（2008AA01Z131）.
    • 2007.1 ~ 2009.12，国家科技支撑计划子课题，村镇教育资源远程服务关键技术研究（2006BAJ07B06-2）.
    • 2005.1 ~ 2007.12，陕西省自然科学基金项目，面向非结构文本的领域知识获取及可计算化研究.
    • 2002.1 ~ 2003.12，教育部“行动计划”中央财政专项，计算机教学管理(CMI)示范系统.


    • 2022年度陕西省自然科学一等奖： 大数据知识工程理论、方法及重大应用（第6贡献人）.
    • 2022年国家教学成果二等奖： 建基地 创模式 搭平台 聚资源，打造“一带一路”工程科技人才培养新体系（第4贡献人）.
    • 2020年中国自动化学会科技进步特等奖： 知识森林个性化智能导学技术及其重大应用（第3贡献人）.
    • 2018年陕西省技术发明一等奖： 场景感知的知识地图导航移动学习关键技术及其应用（第2贡献人）.
    • 2017年国家科技进步奖二等奖： 税务大数据计算与服务关键技术及其应用（第7贡献人）.
    • 2013年中国电子学会科技进步一等奖： 国家电子税务大数据分析关键技术及其应用（第4贡献人）.
    • 2012年湖北省科技进步三等奖： 村镇教育资源配置与远程 服务关键技术及应用（第2贡献人）.
    • 2009年国家教学成果二等奖： 开放式数字教学资源共享模式探索、平台研究与应用实践（第3贡献人）.
    • 2006年国家科技进步奖二等奖： 天地网远程教育关键技术、系列产品及其应用（第3贡献人）.
    • 2003年陕西省科技进步一等奖： 基于IP网的远程教学系统（第2贡献人）.


  • 2025

    Journal Papers

    • Bo Li, Lingling Zhang, Tingting Bao, Yunkuo Lei, Xiaoqing Zhang, Jun Liu. When Multi-focus Image Fusion Meets Nonlinear Spiking Neural P Systems. IEEE TMM, Accepted.
    • Yaxian Wang, Bifan Wei, Yinghong Ma, Lingling Zhang, Xudong Jiang, Henghui Ding, Jun Liu. GeoTree: A Dynamic Tree-based Geometry Problem Solver through LLM-Symbolic Reasoning. IEEE TMM, Accepted.
    • Wenjun Wu, Lingling Zhang, Jun Liu, Ming Ren, Xin Hu, Jiaxin Wang, Qianying Wang. Hierarchy-Based Diagram-Sentence Matching on Dual-Modal Graphs. Pattern Recognition, Accepted.
    • Haochen Han, Minnan Luo, Huan Liu, Fang Nan, Jun Liu. A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels. IEEE TNNLS, Accepted.
    • Fangzhi Xu, Qika Lin, Jiawei Han, Tianzhe Zhao, Jun Liu, Erik Cambria. Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond. IEEE TKDE, Accepted.
    • Qinghua Zheng, Huan Liu, Xiaoqing Zhang, Caixia Yan, Xiangyong Cao, Tieliang Gong, Yong-Jin Liu, Bin Shi, Zhen Peng, Xiaocen Fan, Ying Cai, Jun Liu. Machine Memory Intelligence: Inspired by Human Memory Mechanisms. Engineering, Accepted.
    • Lingling Zhang, Wenjun Wu, Jun Liu, Xiaojun Chang, Xin Hu, Xuan Luo, Yaqiang Wu, Qinghua Zheng. LFSRM: Few-Shot Diagram-Sentence Matching via Local-Feedback Self-Regulating Memory. IEEE TPAMI, Accepted.

    Conference Papers

    • Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, Jun Liu, Yu Qiao, Zhiyong Wu. Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models. ACL 2025.
    • Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu. Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning. ACL 2025.
    • Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Jun Liu, Qika Lin, Zhiyong Wu. φ-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation. ACL 2025.
    • Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu. PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning. ACL 2025.
    • Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu. SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding. KDD 2025.
    • Lingyun Song, Xiaofan Sun, Xinbiao Gan, Yudai Pan, Xiaolin Han, Jie Ma, Jun Liu, Xuequn Shang. Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction. IJCAI 2025.
    • Yaxian Wang, Bifan Wei, Jun Liu, Lingling Zhang, Shuting He, Jun Li, Qika Lin. GlFoMR: A Glance-then-Focus Multimodal Reasoning Framework for Diagram Question Answering. SIGIR 2025.
    • Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Haiping Zhu, Yudai Pan, Jun Liu. Rethinking Continual Knowledge Graph Embedding: Benchmarks and Analysis. SIGIR 2025.
    • Zeao Tu, Xiangdi Meng, Yu He, Zihan Yao, Tianyu Qi, Jun Liu, Ming Li. ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis. NAACL 2025.
    • Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Jun Liu, Xuequn Shang. Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision. WWW 2025.
    • Jie Ma, Zhitao Gao, Qi Chai, Wangchun Sun, Pinghui Wang, Hongbin Pei, Jing Tao, Lingyun Song, Jun Liu, Chen Zhang, Lizhen Cui. Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models. AAAI 2025.
    • Muye Huang, Han Lai, Xinyu Zhang, Wenjun Wu, Jie Ma, Lingling Zhang, Jun Liu. EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding. AAAI 2025.
    • Muye Huang, Lingling Zhang, Han Lai, Wenjun Wu, Xinyu Zhang, Jun Liu. VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning. AAAI 2025.
    • Yaxian Wang, Henghui Ding, Shuting He, Xudong Jiang, Bifan Wei, Jun Liu. Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension. AAAI 2025.

    2024

    Journal Papers

    • Lingling Zhang, Yujie Zhong, Qinghua Zheng, Jun Liu, Qianying Wang, Jiaxin Wang, Xiaojun Chang. TDGI: Translation-Guided Double-Graph Inference for Document-Level Relation Extraction. IEEE TPAMI, 2024, Accepted.
    • Shaowei Wang, Lingling Zhang, Wenjun Wu, Tao Qin, Xinyu Zhang, Jun Liu. Alignment-guided Self-supervised Learning for Diagram Question Answering. IEEE TMM, 2024, Accepted.
    • Bo Li, Lingling Zhang, Jun Liu, Hong Peng. Multi-focus Image Fusion with Parameter Adaptive Dual Channel Dynamic Threshold Neural P Systems. Neural Networks, 2024, Accepted.
    • Song Lingyun, Shang Xuequn, Zhou Ruizhi, Liu Jun, Ma Jie, Li Zhanhuai, Sun Mingxuan. A Multi-Group Multi-Stream Attribute Attention Network for Fine-Grained Zero-Shot Learning. Neural Networks, 2024, Accepted.
    • Lingling Zhang, Yifei Li, Qianying Wang, Yun Wang, Hang Yan, Jiaxin Wang, Jun Liu. FPrompt-PLM: Flexible-Prompt on Pretrained Language Model for Continual Few-Shot Relation Extraction. IEEE TKDE, 2024, Accepted.
    • Hongwei Zeng, Bifan Wei, Jun Liu. RTRL: Relation-aware Transformer with Reinforcement Learning for Deep Question Generation. Knowledge-Based Systems, 2024, Accepted.
    • Mengyue Liu, Jun Liu, Yixiang Dong, Rui Mao, Erik Cambria. Interest-Driven Community Detection on Attributed Heterogeneous Information Networks. Information Fusion, 2024, Accepted.
    • Xinyu Zhang, Lingling Zhang, Xin Hu, Jun Liu, Shaowei Wang, Qianying Wang. Alignment Relation is What You Need for Diagram Parsing. IEEE TIP, 2024, Accepted.
    • Jie Ma, Pinghui Wang, Dechen Kong, Zewei Wang, Jun Liu, Hongbin Pei, Junzhou Zhao. Robust Visual Question Answering: Datasets, Methods, and Future Challenges. IEEE TPAMI, 2024, Accepted.
    • Yudai Pan, Jun Liu, Tianzhe Zhao, Lingling Zhang, Qianying Wang. Context-Aware Commonsense Knowledge Graph Reasoning with Path-Guided Explanations. IEEE TKDE, 2024, Accepted.
    • Bin Shang, Yinliang Zhao, Jun Liu. Knowledge Graph Representation Learning with Relation-guided Aggregation and Interaction. Information Processing & Management, 2024, 61(4), 103752.
    • Shaowei Wang, Lingling Zhang, Tao Qin, Jun Liu, Yifei Li, Qianying Wang, Qinghua Zheng. Multi-View Cognition with Path Search for One-Shot Part Labeling. Computer Vision and Image Understanding (CVIU), 2024, Accepted.
    • Dailusi Ma, Haiping Zhu, Siji Liao, Yan Chen, Jun Liu, Feng Tian, Ping Chen. Learning Path Recommendation with Multi-behavior User Modeling and Cascading Deep Q Networks. Knowledge-Based Systems, 2024, 294, 11743.
    • Bin Shang, Yinliang Zhao, Jun Liu. Learnable Convolutional Attention Network for Knowledge Graph Completion. Knowledge-Based Systems, 2024, Accepted.

    Conference Papers

    • Jie Ma, Min Hu, Pinghui Wang, Wangchun Sun, Lingyun Song, Hongbin Pei, Jun Liu, Youtian Du. Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering. NeurIPS, 2024.
    • Weiping Fu, Bifan Wei, Jianxiang Hu, Zhongmin Kai, Jun Liu. QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation. EMNLP 2024.
    • Jiaxin Wang, Lingling Zhang, Wee Sun Lee, Yujie Zhong, Liwei Kang, Jun Liu. When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models. ACL 2024.
    • Fangzhi Xu, Qika Lin, Tianzhe Zhao, Jiawei Han, Jun Liu. PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering. ACL 2024.
    • Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu. Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models. ACL 2024.
    • Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Jun Liu. Untargeted Adversarial Attack on Knowledge Graph Embeddings. SIGIR 2024.
    • Wenjun Wu, Lingling Zhang, Jun Liu, Xi Tang, Yaxian Wang, Shaowei Wang, Qianying Wang. E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator. CVPR 2024.
    • Shaowei Wang, Lingling Zhang, Longji Zhu, Tao Qin, Kim-Hui Yap, Xinyu Zhang, Jun Liu. CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering. CVPR 2024.
    • Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu. A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction. COLING 2024.
    • Yudai Pan, Jun Liu, Tianzhe Zhao, Lingling Zhang, Yun Lin, Jinsong Dong. A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction. WWW 2024.
    • Bin Shang, Yinliang Zhao, Jun Liu, Di Wang. Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion. AAAI 2024.
    • Bin Shang, Yinliang Zhao, Jun Liu, Di Wang. LAFA: Multimodal Knowledge Graph Completion with Link Aware Fusion and Aggregation. AAAI 2024.

    2023

    Journal Papers

    • Jie Ma, Jun Liu, Qi Chai, Pinghui Wang, Jing Tao. Diagram Perception Networks for Textbook Question Answering via Joint Optimization. IJCV, 2023, Accepted.
    • 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, Kevin Chen-Chuan Chang. 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, Qinghua 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. Diagram Visual Grounding: Learning to See with Gestalt-Perceptual Attention. IJCAI 2023.
    • Hongwei Zeng, Jun Liu, Bifan Wei, 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, 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, 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, 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, Honghui Chen. Multistructure Contrastive Learning for Event Representation. IEEE TNNLS, 2022, Accepted.
    • Lingling Zhang, Shaowei Wang, Jun Liu, Xiaojun Chang, Qika Lin, Yaqiang Wu, 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, Maarten de Rijke. Generating Relevant and Informative Questions for Open-domain Conversations. ACM TOIS, 2022, Accepted.
    • Shaowei Wang, Lingling 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, Qianying Wang. Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations. EMNLP 2022.
    • Jiaxin Wang, Lingling Zhang, Jun Liu, Liang Xi, Yujie Zhong, 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, 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, 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, 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, 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, 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, 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, Jun Liu. Fuzzy c-Means Clustering with Discriminative Projection. ICBK 2021.
    • Hongwei Zeng, Zhenjie Hong, Jun Liu, 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, 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, 刘烣, 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, 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, Qika Lin. Jointly Optimized Neural Coreference Resolution with Mutual Attention. WSDM 2019.
    • Ruijie Wang, Meng Wang, Jun Liu, Michael Cochez, 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, 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, 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, Jun Liu. Graph Embedding based Query Construction over Knowledge Graphs. IEEE ICBK 2018. (Best Paper Award)
    • Ruoqing Ren, Haimeng Duan, Wenqiang Liu, 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, Jun Liu. 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 BasedMand 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, 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, Qinghua Zheng. Opinioned Post Detection in Sina Weibo. IEEE Access, 2017, 5(1): 7263-7271.
    • Jun Liu, Zheng Yan, Athanasios V. Vasilakos, 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, 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, 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, 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, Jun Liu. DKG: An Expanded Knowledge Base for Online Course. DMMOOC 2017.
    • Wenqiang Liu, Jun Liu, Haimeng Duan, Wei Hu, 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, 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, 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, Ting Liu. Frequent Subgraph based Familial Classification of Android Malware. ISSRE 2016. (Best Paper Award)
    • Siyu Yao, Jun Liu, Meng Wang, Bifan Wei, Xuelu Chen. [Demo] ANNA: Answering Why-Not Questions for SPARQL. ISWC 2015.
    • Minnan Luo, Lingling Zhang, Qinghua Zheng, 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. CIKM 2014.
    • Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng. DFT-extractor: A System to Extract Domain-specific Faceted Taxonomies from Wikipedia. WWW 2013.
    • Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng. MOTIF-RE: Motif-based Hypernym/hyponym Relation Extraction from Wikipedia Links. ICONIP 2012.
    • Jun Liu, Lu Jiang, Zhaohui Wu, Qinghua Zheng. Mining Preorder Relation between Knowledge Units from Text. ACM SAC 2010.