This study introduces an algorithm to construct tag trees that can be used as a user-friendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.
Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the
The proposed algorithm has been compared with a well-established solution
A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.
The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text.
Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold: (1) an algorithm was developed to construct tag trees with consideration given to both semantic coherence and structural balance and (2) the effectiveness of a node generality metric,