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.
@article{arxiv.2501.11499,
title = {KEIR @ ECIR 2025: The Second Workshop on Knowledge-Enhanced Information Retrieval},
author = {Zihan Wang and Jinyuan Fang and Giacomo Frisoni and Zhuyun Dai and Zaiqiao Meng and Gianluca Moro and Emine Yilmaz},
journal= {arXiv preprint arXiv:2501.11499},
year = {2025}
}