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Abstract

Based on text mining, this study explored topics in the research domain of knowledge organization. A text corpus consisting of titles and abstracts was generated from 282 articles of the Knowledge Organization journal for the recent ten years from 2006 to 2015. Term frequency analysis and Latent Dirichlet allocation topic modeling were employed to analyze the collected corpus. Topic modeling uncovered twenty research topics prevailing in the knowledge organization field, including theories and epistemology, classification scheme, domain analysis and ontology, digital archiving, document indexing and retrieval, taxonomy and thesaurus system, metadata and controlled vocabulary, ethical issues, and others. In addition, topic trends over the tenyears were examined to identify topics that attracted more discussion in the journal. The top two topics that received increased attention recently were "ethical issues in knowledge organization" and "domain analysis and ontologies." This study yields insight into a better understanding of the research domain of knowledge organization. Moreover, text mining approaches introduced in this study have methodological implications for domain analysis in knowledge organization.

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Abstract

The purpose of this study is to understand users' motivations and intentions in the use of institutional collections on social tagging sites. Previous social tagging studies have collected social tagging data and analyzed how tagging functions as a tool to organize and retrieve information. Many studies focused on the patterns of tagging rather than the users' perspectives. To provide a more comprehensive picture of users' social tagging activities in institutional collections, and how this compares to social tagging in a more personal context, we collected data from social tagging users by surveying 7,563 participants in the Library of Congress's Flickr Collection. We asked users to describe their motivations for activities within the LC Flickr Collection in their own words using open-ended questions. As a result, we identified 11 motivations using a bottom-up, open-coding approach: affective reactions, opinion on photo, interest in subject, contribution to description, knowledge sharing, improving findability, social network, appreciation, personal use, and personal relationship. Our study revealed that affective or emotional reactions play a critical role in the use of social tagging of institutional collections by comparing our findings to existing frameworks for tagging motivations. We also examined the relationships between participants' occupations and our 11 motivations.