Knowledge Graph Reasoning and Its Applications

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.