[论文解读] Building Faculty Expertise Ontology using Protege: Enhancing Academic Library Research Services

Snehasish Paul|arXiv (Cornell University)|Jan 12, 2026

Expert finding and Q&A systems被引用 0

一句话总结

本论文提出一个基于 Protégé 的教师专长本体，采用分层的顶层/中层/底层结构来映射系别、学科、教师和联系信息，支持图灵查询（SPARQL）和 library 研究服务的转介。

ABSTRACT

Academic libraries struggle to find and access faculty expertise across disciplines. This research proposes a faculty expertise ontology with a hierarchical structure based on Protégé to enhance library services and knowledge organisation. The ontology classifies relationships between departments, subject areas, faculty members, and contact data into layers including Top, Middle, and Bottom levels. The academic structure that this tiered form takes enables discovery of expertise in departments. The ontology which answers competency questions generated from the subject matter experts can answer real-world questions like which faculties are in the specific areas, how to collaborate with other disciplines and search contact information and so on. Competency questions act as design and test instruments to show that the ontology will fulfil the information needs of Researchers, Librarians and Administrators. The ontology is able to cope with semantically-enhanced queries, as shown by SPARQL implementations. The model works effectively in initiating referrals to an expert, aligning research with the strength of a department and allowing academics to partner up. The ontology delivers a scalable platform that adapts to institutional change. In the future, we intend to integrate with institutional databases and library systems for automatic API updates, as well as develop user interfaces and visualisations.

研究动机与目标

• 促进对学术图书馆跨学科教师专长高效访问的需求认知。
• 设计一个将系别、学科领域、教师及联系信息连接起来的分层本体。
• 展示能力问题如何引导本体设计与测试，以满足现实世界的图书馆需求。
• 实现语义增强的查询和转介，支持研究人员、馆员和管理员。

提出的方法

• 在 Protégé 中建立一个分层本体，包含顶层、中层、底层，用以表示系别、学科、教师和联系数据。
• 对实体之间的关系进行编码，以支撑源自主题专家的能力问题。
• 使用 SPARQL 演示对本体的语义查询能力。
• 描述本体如何启动专家转介，并使研究与系别优势对齐。

实验结果

研究问题

• RQ1 在各系别和学科领域可发现哪些教师专长？
• RQ2 本体如何支持跨学科协作并提供可访问的联系信息？
• RQ3 能力问题是否能够有效验证本体以满足研究人员和馆员的信息需求？
• RQ4 模型在语义增强的查询与转介方面的支持程度如何？

主要发现

• 本体能够发现系别专长及潜在的合作机会。
• 它通过 SPARQL 提供语义增强的查询，便于实际研究服务。
• 该模型有助于将研究转介给学科领域专家，并使研究与系别优势对齐。
• 本体设计具有可扩展性，能够适应制度变革。