Purpose

This paper puts forward a MARC-based semiautomated approach to extracting semantically rich subject facets from general and/or specialized controlled vocabularies for display in topic-oriented faceted catalog interfaces in a way that would better support users' exploratory search tasks.

Design/methodology/approach

Hierarchical faceted subject metadata is extracted from general and/or specialized controlled vocabularies by using standard client/server communication protocols. Rigorous facet analysis, classification and linguistic principles are applied on top of that to ensure faceting accuracy and consistency.

Findings

A shallow application of facet analysis and classification, together with poorly organized displays, is one of the major barriers to effective faceted navigation in library, archive and museum catalogs.

Research limitations/implications

This paper does not deal with Web-scale discovery services.

Practical implications

This paper offers suggestions that can be used by the technical services departments of libraries, archives and museums in designing and developing more powerful exploratory search interfaces.

Originality/value

This paper addresses the problem of deriving clearly delineated topical facets from existing metadata for display in a user-friendly, high-level topical overview that is meant to encourage a multidimensional exploration of local collections as well as “learning by browsing.”

中文翻译：

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提高图书馆目录及其他领域主题方面的有效性：基于 MARC 的半自动化方法

目的

本文提出了一种基于 MARC 的半自动方法，从通用和/或专门的受控词汇表中提取语义丰富的主题方面，以更好地支持用户探索性搜索任务的方式显示在面向主题的分面目录界面中。

设计/方法/方法

通过使用标准的客户端/服务器通信协议，从通用和/或专用受控词汇表中提取分层分面主题元数据。在此基础上应用了严格的分面分析、分类和语言原则，以确保分面的准确性和一致性。

发现

分面分析和分类的浅层应用，以及组织不当的展示，是图书馆、档案馆和博物馆目录中有效分面导航的主要障碍之一。

研究限制/影响

本文不涉及 Web 规模的发现服务。

实际影响

本文提供的建议可供图书馆、档案馆和博物馆的技术服务部门在设计和开发更强大的探索性搜索界面时使用。

原创性/价值

本文解决了从现有元数据中导出清晰描绘的主题方面的问题，以显示在用户友好的高级主题概述中，旨在鼓励对本地馆藏的多维探索以及“通过浏览学习”。