The field of knowledge graph embedding and entity resolution is moving towards more robust and efficient methods for handling large-scale datasets and emerging entities. Researchers are exploring new approaches to improve the accuracy and scalability of knowledge graph embedding models, including the use of advanced negative sampling strategies and agent-based frameworks. Additionally, there is a growing interest in developing entity resolution pipelines that can handle high-volume datasets and provide accurate results. Noteworthy papers in this area include: Understanding the Embedding Models on Hyper-relational Knowledge Graph, which proposes a new framework for preserving the original HKG topology and capturing long-range dependencies. AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities, which introduces a novel agent-based framework for dynamically constructing rich knowledge graph triplets. A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolution, which presents a scalable and reliable solution for entity resolution in large datasets.