Need for a systemic theory of classification in information science

Abstract

In the article, the author aims to clarify some of the issues surrounding the discussion regarding the usefulness of a substantive classification theory in information science (IS) by means of a broad perspective. By utilizing a concrete example from the High Accuracy Retrieval from Documents (HARD) track of a Text REtrieval Conference (TREC), the author suggests that the “bag of words” approach to information retrieval (IR) and techniques such as relevance feedback have significant limitations in expressing and resolving complex user information needs. He argues that a comprehensive analysis of information needs involves explicating often-implicit assumptions made by the authors of scholarly documents, as well as everyday texts such as news articles. He also argues that progress in IS can be furthered by developing general theories that are applicable to multiple domains. The concrete example of application of the domain-analytic approach to subject analysis in IS to the aesthetic evaluation of works of information arts is used to support this argument.