Recent advancements in knowledge graphs are focusing on enhancing their applicability across various domains, particularly through improved link prediction and reasoning capabilities. Techniques like THOR are pushing the boundaries of hyper-relational knowledge graphs by enabling inductive link prediction, which allows models to generalize beyond specific vocabularies. Meanwhile, TKG-Thinker is addressing the challenges of temporal knowledge graph question answering by employing agentic reinforcement learning to facilitate dynamic reasoning, thus overcoming limitations of static prompting strategies. Additionally, frameworks like SynergyKGC are tackling the complexities of heterogeneous graph structures, ensuring robust relational reasoning by reconciling topological mismatches. The integration of domain-specific knowledge graphs with general knowledge graphs is also gaining traction, as seen in recent work on knowledge graph fusion, which aims to enhance coverage and relevance. Collectively, these efforts are paving the way for more efficient and effective applications in areas such as information retrieval, recommendation systems, and automated reasoning.