聚焦外部知识融合，提升检索模型对新信息和专业领域的适应能力
KEIR @ ECIR 2025: The Second Workshop on Knowledge-Enhanced Information Retrieval
- 构建知识增强型检索框架，突破预训练模型依赖内部知识的局限
- 推动检索系统在语义理解与领域适配上的性能提升
- 适合关注知识融合、IR系统优化的研究者与工业界开发者
预训练语言模型（如BERT、GPT-4）已成为现代信息检索（IR）系统的基石。然而，现有基于PLM的检索模型主要依赖训练阶段学习到的知识进行预测，难以获取并融入外部最新或特定领域的信息，导致在语义细微差别、上下文相关性及领域特定问题上表现受限。为此，本文提出第二届知识增强信息检索研讨会（KEIR @ ECIR 2025），旨在为融合外部知识的创新方法提供交流平台，以提升信息检索在快速演变技术环境中的有效性。研讨会目标是汇聚学界与产业界研究者，共同探讨知识增强信息检索的多个关键方面。
原文摘要 · Abstract (English)
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.
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