The use of knowledge graphs has gained significant traction in a wide variety of applications, ranging from recommender systems and question answering to fact checking. By leveraging the wealth of information contained within knowledge graphs, it is possible to greatly enhance various downstream tasks, making reasoning over knowledge graphs an area of increasing interest. However, despite its popularity, knowledge graph reasoning remains a challenging problem. The first major challenge of knowledge graph reasoning lies in the nature of knowledge graphs themselves. Most knowledge graphs are incomplete, meaning that they may not capture all the relevant knowledge required for reasoning. As a result, reasoning on incomplete knowledge graphs can be difficult. Additionally, real-world knowledge graphs often evolve over time, which presents an additional challenge. The second challenge of knowledge graph reasoning pertains to the input data. In some KG reasoning applications, users may be unfamiliar with the background knowledge graph, leading to the possibility of asking ambiguous questions that can make KG reasoning tasks more challenging. Moreover, some applications require iterative reasoning, where users ask several related questions in sequence, further increasing the complexity of the task. The third challenge of knowledge graph reasoning concerns the algorithmic aspect. Due to the varied properties of relations in knowledge graphs, such as transitivity, symmetry, and asymmetry, designing an all-round KG reasoning model that fits all these properties can be challenging. Furthermore, most KG reasoning models tend to focus on solving a specific problem, lacking the generalization ability required to apply to other tasks. This tutorial aims to comprehensively review different aspects of knowledge graph reasoning applications and highlight open challenges and future directions. It is intended to benefit researchers and practitioners in the fields of data mining, artificial intelligence, and social science.

•  Introduction

• Part 1: General Knowledge Graph Reasoning

• Part 2: Query-specific Knowledge Graph Reasoning

• Part 3: Future Trends

 Lihui Liu

• Lihui Liu is a Ph.D. student in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He is the corresponding tutor. His research focuses on large-scale data mining and machine learning, particularly on graphs, with an emphasis on knowledge graph reasoning. Lihui's research has been published at several major conferences and journals in data mining and artificial intelligence, and he has served as a reviewer and program committee member for top-tier data mining and artificial intelligence conferences and journals, including KDD, WWW, AAAI, IJCAI, and BigData. 

Hanghang Tong

• Hanghang Tong is currently an associate professor at Department of Computer Science at University of Illinois at Urbana-Champaign. Before that, he worked at Arizona State University as an associate professor, at City University of New York (City College) as an assistant professor and at IBM T. J. Watson Research Center as a Research Staff Member. He received his Ph.D. from the Machine Learning Department of School of Computer Science at Carnegie Mellon University in 2009. His major research interest lies in large-scale data mining for graphs and multimedia. In the past, He has published 200+ papers at these areas and his research has received several awards, including IEEE Fellow (2021), ACM distinguished member (2020), ICDM Tao Li award (2019), SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), and several best paper awards. (e.g., ICDM’06 best paper, SDM’08 best paper, CIKM’12 best paper, etc.). He was Editor-in-Chief of ACM SIGKDD Explorations (2018 – 2022).