Visual Distinctive Language: Using a Hypertopic-Based Iconic Tagging System for Knowledge Sharing
Abstract
Tagging systems for social semantic web centralize and provide the tags that can be employed in classifying, sharing and seeking knowledge on the web for personal or organizational use. However, an increased variety of vocabularies and languages cause connections between tags and documents marked by these textual tags to become less and less distinctive, making the use and reuse of tagging systems even harder. In this paper, we present an approach of Visual Distinctive Language to improve the representation of tags and their structure. This approach was also evaluated by a control experiment with an observation of tagging process, the results of which have validated our hypothesis.