CALA AP Chapter’s September Webinar 2024: Session 1

Session 1: The Impact of AI on Knowledge Access and Discovery: An Assessment of Present and Future Developments (人工智能对知识获取与发现的影响: 现状与未来发展的评估)

Date (日期): September 26, 2024 (2024年9月26日)

Time (时间): 9:00-10:00 AM Beijing Time (北京时间上午9-10点)

Registration Link (注册链接): https://nyu.zoom.us/meeting/register/tJEvceCurTsoG9GbLoXsdD6r1yeEByYdX4Fh#/registration

Language (语言): Chinese (中文)

Speaker: Charlene Chou is the Head of Knowledge Access Department at the New York University Libraries, managing cataloging and metadata services. She has been actively serving on various committees, including the PCC (Program for Cooperative Cataloging) Policy Committee, the RDA Steering Committee, the OCLC RLP Metadata Manager Group, and the Joint RDA Board and RSC Working Group on Artificial Intelligence. She has committed to do pilot projects on emerging trends and technologies. Her research interests lie primarily in the areas of metadata management, the discovery of multilingual resources, artificial intelligence/natural language processing models for subject indexing, digital scholarship, and inclusive metadata.

主讲人简介：周小玲，纽约大学图书馆知识获取部主任，负责管理编目与元数据服务。她积极参与各种国际委员会，包括PCC（合作编目计划）政策委员会、RDA指导委员会、OCLC RLP（研究型图书馆合作计划）元数据管理小组，以及RDA联合委员会与RDA指导委员会人工智能工作组，并担任社区推广参与官员。她致力于开展关于新兴趋势和技术的试点项目，研究兴趣主要集中在元数据管理、多语种资源发现、数字人文与数字图书馆用于主题索引的人工智能/自然语言处理模型、数字学术以及包容性元数据等领域。

Abstract: This presentation aims to explore how to improve the discoverability of library resources by strategically using AI/LLM while mitigating associated harms such as hallucinations, data quality issues, bias, copyright concerns, and ethical challenges. In recent years, library metadata communities have strived to pilot and test linked data, entity management, and knowledge graphs, with the goal of enhancing resource discoverability through structured metadata. In addition to sharing test results from using AI/LLM and NLP tools to improve the discovery of library resources and metadata management, the presenter will provide insights into the AI surveys and initiatives undertaken by the PCC (Program for Cooperative Cataloging) and RDA Steering Committee, offering a holistic view of future directions and activities. While concerns about AGI and AI-related harms persist, semantic AI–which integrates machine learning and knowledge graphs–appears poised to become the next generation of AI, offering both machine optimization and transparency in underlying knowledge models. The library community may benefit from adopting a semantic AI approach, especially in the context of the research data lifecycle.

本次研讨会旨在探讨如何通过战略性地使用AI/LLM（人工智能/大型语言模型）来提高图书馆资源的可发现性，同时化解与之相关的潜在风险问题，如虚假信息、数据质量问题、偏见、版权问题和伦理挑战。近年来，图书馆元数据社区一直致力于试验和测试关联数据、实体管理和知识图谱，旨在通过结构化的元数据提升资源的可发现性。除了分享使用AI/LLM和NLP（自然语言处理）工具改进图书馆资源发现和元数据管理的测试结果外，主讲人还将提供有关PCC（合作编目计划）和RDA指导委员会所开展的AI调查和倡议的见解，全面展望未来的方向和活动。尽管对AGI（通用人工智能）和人工智能相关危害的担忧依然存在，但语义人工智能——它集成了机器学习和知识图谱——似乎有望成为下一代人工智能，增加了底层知识模型的机器优化和透明度。从研究数据生命周期的角度来分析，图书馆界可能从采用语义人工智能方法中获益与改进。