Pretrained language models (PLMs) like BERT and GPT-4 have become the foundation for modern information retrieval (IR) systems. However, existing PLM-based IR models primarily rely on the knowledge learned during training for prediction, limiting their ability to access and incorporate external, up-to-date, or domain-specific information. Therefore, current information retrieval systems struggle with semantic nuances, context relevance, and domain-specific issues. To address these challenges, we propose the second Knowledge-Enhanced Information Retrieval workshop (KEIR @ ECIR 2025) as a platform to discuss innovative approaches that integrate external knowledge, aiming to enhance the effectiveness of information retrieval in a rapidly evolving technological landscape. The goal of this workshop is to bring together researchers from academia and industry to discuss various aspects of knowledge-enhanced information retrieval.

翻译：以BERT和GPT-4为代表的预训练语言模型已成为现代信息检索系统的技术基石。然而，现有基于预训练语言模型的信息检索方法主要依赖训练阶段习得的知识进行预测，难以有效获取并融合外部知识、实时信息或领域特定内容。因此，当前信息检索系统在处理语义细微差异、上下文相关性及领域适应性问题时仍面临显著挑战。为应对这些难题，我们发起第二届知识增强信息检索研讨会（KEIR @ ECIR 2025），旨在构建学术交流平台，探讨融合外部知识的创新方法，以提升技术快速演进背景下信息检索的效能。本次研讨会致力于汇聚学术界与工业界的研究人员，共同深入探讨知识增强信息检索的多维度议题。