A semantic-based approach for searching and browsing tag spaces

In this paper we propose the Semantic Tag Clustering Search (STCS) framework for enhancing the user experience in interacting with tagging systems. This framework consists of three parts. The first part deals with syntactic variations by finding clusters of tags that are syntactic variations of each other and assigning labels to them. The second part of the framework addresses the problem of the lack of semantics in tagging systems by recognizing contexts and constructing semantic clusters for tags. The last, and final part of the STCS framework, utilizes the clusters obtained from the first two parts to improve the search and exploration of tag spaces. For removing syntactic variations, we use the normalized Levenshtein distance and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we employ two non-hierarchical and two hierarchical clustering techniques. To evaluate the value of the semantic clusters, we develop a Web application called XploreFlickr.com for searching and browsing through Flickr resources.

► We address the problem of syntactic and semantic variations of tags. ► Our syntactic variation algorithm achieves a precision of 95%. ► We compare four semantic clustering techniques that address semantic tag variations. ► Our non-hierarchical semantic clustering algorithm has best precision. ► Our hierarchical semantic clustering algorithm finds the most correct relations.