The 6th International Workshop on
Knowledge Graph Management and Applications
(KGMA 2023)

Workshop Web

Workshop Topics

In recent years, an increasing number of large-scale knowledge graphs have been constructed and published on the Web, by both academic and industrial communities, such as DBpedia, YAGO, Freebase, Wikidata, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, and others. In fact, a knowledge graph is essentially a large network of entities, their properties, and semantic relationships between entities. Such kind of graph-based knowledge data has been posing a great challenge to the traditional data management theories and technologies. On the other hand, the database community has been putting a lot of effort into graph databases for nearly two decades to make the storage, query processing, mining, and analysis of large graph data more efficient and scalable. However, there are still gaps between the requirements of knowledge graph applications from various domains and the current state of techniques in graph databases. Therefore, this workshop aims to bring together researchers, practitioners, developers, and users from knowledge graph research and application, graph database, social network, and other relevant communities to address the challenges, present state-of-the-art solutions, exchange ideas and results, and discuss future research directions in management, analysis, and application of knowledge graphs in different domains.

The topics of interest include, but are not limited to the following:

Knowledge graph organization and construction:

  • Knowledge graph information extraction
  • Knowledge graph data integration
  • Knowledge graph construction
  • Knowledge graphs and knowledge representation
  • Knowledge graphs and knowledge bases
  • Knowledge graphs and knowledge engineering
  • Knowledge graphs and ontologies
  • Knowledge graphs in social networks
  • Probabilistic and uncertain knowledge graphs

Knowledge graph and graph data storage:

  • Knowledge graph storage and indexing
  • RDF graph storage and indexing
  • Graph database storage scheme
  • Distributed knowledge graph storage and indexing
  • Relational-based knowledge graph storage and indexing

Knowledge graph query processing:

  • Graph pattern matching
  • Reachability query processing
  • Shortest path query processing
  • Regular path query processing
  • Navigational query processing
  • Graph query languages
  • Distributed/parallel graph query processing
  • Knowledge graph query processing and benchmarking

Knowledge graph learning and data mining:

  • Knowledge graph embedding and representational learning
  • Graph classification
  • Graph clustering
  • Graph frequent pattern mining
  • Link prediction in knowledge graphs
  • Outlier detection in knowledge graphs
  • Deep learning on knowledge graphs

Knowledge graph analysis and applications:

  • Knowledge graph data visualization
  • Social network analysis using knowledge graphs
  • Knowledge-graph based inference and reasoning
  • Knowledge-graph based information retrieval
  • Knowledge-graph based recommendation systems
  • User interfaces of knowledge-graph based systems
  • Questing answering using knowledge graphs
  • Knowledge-graph based intelligent systems
  • Knowledge-graph based information systems
  • Knowledge graphs and natural language processing
  • Biological and biomedical knowledge graphs
  • Knowledge-graph based bioinformatics
  • Security, privacy, and trust on knowledge graphs


Workshop Co-Chairs:

  1. Xiang Zhao, National University of Defense Technology, China
  2. Xin Wang, Tianjin University, China

Program Committee Members:

  • Huajun Chen, Zhejiang University, China
  • Wei Hu, Nanjing University, China
  • Saiful Islam, Griffith University, Australia
  • Jiaheng Lu, University of Helsinki, Finland
  • Jianxin Li, Deakin University, Australia
  • Ronghua Li, Beijing Institute of Technology, China
  • Jeff Z. Pan, University of Aberdeen, UK
  • Jijun Tang, University of South Carolina, USA
  • Haofen Wang, Tongji University, China
  • Hongzhi Wang, Harbin Institute of Technology, China
  • Junhu Wang, Griffith University, Australia
  • Meng Wang, Southeast University, China
  • Xiaoling Wang, East China Normal University, China
  • Xuguang Ren, G42 Inception Institute of Artificial Intelligence, UAE
  • Guohui Xiao, Free University of Bozen-Bolzano, Italy
  • Zhuoming Xu, Hohai University, China
  • Qingpeng Zhang, City University of Hong Kong, China
  • Xiaowang Zhang, Tianjin University, China
  • Weiren Yu, University of Warwick, UK


KGMA Invited Talk

Dr. Dandan Song

Title: Research of Entity Alignment for Knowledge Graphs


The talk is about the recent developments in entity alignment research within knowledge graphs. It will provide a preliminary overview of research challenges and directions in recent papers presented at major international conferences over the past two years. Challenges and achievements in entity alignment across seven aspects: data labeling, effective information extraction, computational efficiency, similarity computation, dynamic changes, multi-modal entity alignment, and continuous entity alignment will be introduced with insights into the future.


Dr. Dandan Song is a tenured professor in School of Computer Science and Technology, Beijing Institute of Technology. She received her B.S. and Ph.D. degrees from the Department of Computer Science and Technology, Tsinghua University in 2004 and 2009, respectively. Her research interests include knowledge mining, natural language processing, and bioinformatics. She has published many papers in Nature sub-journal, ACL, SIGIR, IJCAI, AAAI, WWW and other top international conferences and important academic journals.

Paper Presentation

1. A Bidirectional Question-Answering System using Large Language Models and Knowledge Graphs

Lifan Han (College of Intelligence and Computing, Tianjin University), Xin Wang⋆ (College of Intelligence and Computing, Tianjin University), Zhao Li (College of Intelligence and Computing, Tianjin University) , Heyi Zhang (College of Intelligence and Computing, Tianjin University), and Zirui Chen (College of Intelligence and Computing, Tianjin University)


2. A Comprehensive Review of Relation Prediction Techniques in Knowledge Graph

Yuxuan Lu (Guangzhou University), Shiyu Yang⋆ (Guangzhou University), and Benzhao Tang (Guangzhou University)


3. Negation: An Effective Method to Generate Hard Negatives

Yaqing Sheng (Laboratory for Big Data and Decision, National University of Defense Technology), Weixin Zeng (Laboratory for Big Data and Decision, National University of Defense Technology), and Jiuyang Tang⋆ (Laboratory for Big Data and Decision, National University of Defense Technology)