Neuro-symbolic relation extraction lies at the intersection of neural networks and symbolic reasoning, presenting promising opportunities to enhance the capabilities of natural language processing (NLP) systems. Despite its potential, a comprehensive review of how these systems are developed and applied to the task of relation extraction has been lacking. This chapter addresses this gap by offering an in-depth overview of the current landscape in neuro-symbolic relation extraction, focusing on key methodologies and the datasets utilized in this field. We systematically categorize existing approaches, emphasizing how they integrate neural and symbolic components to tackle various challenges and the types of information they incorporate. Additionally, we review the datasets used to evaluate neuro-symbolic relation extraction systems, detailing their statistics, creation processes, and underlying domains. Furthermore, we discuss future research directions and challenges, such as the analysis of symbolic information and the integration of datasets with existing knowledge graphs. By synthesizing these findings, this chapter aims to provide researchers and practitioners with a clear understanding of the state of neuro-symbolic relation extraction and to inspire further innovations in this rapidly evolving field.