The field of knowledge graphs and information extraction is rapidly evolving, with a focus on improving the accuracy and efficiency of extracting relevant information from large datasets. Recent developments have seen the integration of sentiment analysis and role clustering to enhance fake news detection, as well as the application of graph neural networks and large language models to improve multi-view multi-label feature selection and document-level relation extraction. Additionally, there has been a growing interest in developing digital libraries and platforms to support the reuse and retrieval of scientific knowledge, such as the ORKG reborn digital library. Noteworthy papers in this area include the SARC framework for fake news detection, which utilizes sentiment-enhanced deep clustering to identify user roles, and the RelPrior paradigm for document-level relation extraction, which uses binary relation as a prior to extract and determine correlated entity pairs. The GSAP-ERE dataset has also been introduced, providing a fine-grained scholarly entity and relation extraction focused on machine learning. These advancements have the potential to significantly improve the efficiency and accuracy of information extraction and knowledge graph construction, with applications in a wide range of fields, including science, social media, and recommendation systems.