PurposeThis paper seeks to describe and discuss a tagging experiment involving images related to Israeli and Jewish cultural heritage. The aim of this experiment was to compare freely assigned tags with values (free text) assigned to predefined metadata elements.Design/methodology/approachTwo groups of participants were asked to provide tags for 12 images. The first group of participants was asked to assign descriptive tags to the images without guidance (unstructured tagging), while the second group was asked to provide free‐text values to predefined metadata elements (structured tagging).FindingsThe results show that on the one hand structured tagging provides guidance to the users, but on the other hand different interpretations of the meaning of the elements may worsen the tagging quality instead of improving it. In addition, unstructured tagging allows for a wider range of tags.Research limitations/implicationsThe recommendation is to experiment with a system where the users provide both the tags and the context of these tags.Originality/valueUnstructured tagging has become highly popular on the web, thus it is important to evaluate its merits and shortcomings compared to more conventional methods.

In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the "wisdom of the crowds" helps overcome the difficulties due to the lack of information.

Purpose -The purpose of this study was to compare the ease of use and the effectiveness of several interfaces for retrieving tagged images. Design/methodology/approach -A number of participants were randomly assigned to one of four retrieval interfaces: tag search in a search box; faceted tag search in a search box; selecting terms from the tag cloud of all the tags in the database; and selecting concepts from an ontology created from the tags assigned to the images. Each interface was tested by 21 users. Findings -The results show that the highest recall on average was achieved by users of the ontology interface, for seven out of the ten tasks, however, users were more satisfied with the textbox-based search than the cloud or the ontology.Research limitations/implications -The experiment was rather specific, and more studies are needed in order to generalize the findings. Originality/value -With the widespread use of tagging on the web it is of importance to examine whether tagging enables resource discovery. This study shows that in addition to the tags, the retrieval interface also influences user satisfaction and retrieval success.

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The research objective of this work is to develop a general framework that incorporates collaborative social tagging with a novel ontology scheme conveying multiple perspectives. We propose a framework where multiple users tag the same object (an image in our case), and an ontology is extended based on these tags while being tolerant about different points of view. We are not aware of any other work that attempted to devise such an environment and to study its dynamics. The proposed framework characterizes the underlying processes for controlled collaborative development of a multi-perspective ontology and its application to improve image annotation, searching and browsing. Our case study experiment with a set of selected annotated images indicates the soundness of the proposed ontological model.