Problem
Research questions and friction points this paper is trying to address.
grey literature
Calls for Papers
Metascience
Scientometric analysis
unstructured data
A Pathway for Assessing Grey Literature: Leveraging AI to Extract Conference Metadata and Organiser Information from Calls for Papers
🤖 AI Summary
本文提出COCI框架，利用AI技术从征稿启事中提取会议元数据和组织者信息，解决灰色文献因格式异质难以处理的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Metadata Extraction
Author Disambiguation
Semantic Mapping
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