AI 中文总结
本文提出基于AI的框架COCI，通过多阶段实体提取等流程从征稿启事自动提取结构化元数据，为灰色文献系统分析奠定基础，助力非出版商主办活动的相关研究。
AI 中文摘要
尽管灰色文献（包括征稿启事（Calls for Papers, CfPs））具有重要价值，但由于其非结构化、高度异质的格式，传统工具难以大规模处理，因此在元科学与计量学分析中仍被大量忽视。然而，大型语言模型如今提供了关键机遇，可用于设计创新工具以系统采集和处理此类数据。本文提出COCI，一种基于AI的框架，能从原始征稿启事文本中自动提取细粒度结构化元数据。COCI采用多阶段实体提取流程，随后针对OpenAlex进行作者消歧，并对主题与会议系列进行语义映射。该流程可识别关键数据点，包括会议届次、地理位置，以及主办方及其具体角色、所属机构的完整列表。通过将此前无法获取的信息结构化，COCI为灰色文献的系统分析奠定了基础，开辟了新的研究机遇，并将学术研究重点转向非出版商主办的活动。
英文摘要
Despite its importance, grey literature, including Calls for Papers (CfPs), remains largely overlooked in Metascience and Scientometric analysis due to its unstructured, highly heterogeneous format, which traditional tools struggle to process at scale. However, Large Language Models now offer a pivotal opportunity to devise innovative tools for systematically harvesting and processing such data. In this paper, we introduce COCI, an AI-based framework that automates the extraction of granular, structured metadata from raw CfP text. COCI employs a multi-stage pipeline for entity extraction, followed by author disambiguation against OpenAlex and semantic mapping of topics and conference series. This process identifies key data points, including conference editions, geographic locations, and comprehensive lists of organisers, along with their specific roles and affiliations. By structuring this previously inaccessible information, COCI establishes a foundation for the systematic analysis of grey literature, enabling new research opportunities and shifting the scholarly focus towards non-publisher-based events.
CommentsPaper accepted at STI-ENID 2026 https://www.uantwerpen.be/en/conferences/30th-annual-international-conference-on-science-and-technology-indicators/
