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Although tagging simplifies resource browsing and retrieval, it suffers from several issues: among them are redundancy and ambiguity. In this work we focus on the problem of resolving tag word-sense ambiguity within a typical semi-automatic tagging procedure. In that process a user proposes a tag for a resource, if the tag is found to be related to more than one context, she is provided with two or more cues among which to choose, so as to remove the tag ambiguity. Key phases, in such a disambiguation procedure, are ambiguous tag detection and cue discovery. Both should rely on effective word-to-context relatedness metrics. Among the most effective relatedness metrics are those defined on the basis of a feature vector representation of the words. In this work we compare different word-to-context relatedness metrics in terms of effectiveness within the disambiguation process. We propose to use a metrics derived from a Maximum Likelihood estimator of the Jensen-Shannon Divergence among feature-count histograms and we show that such a metrics performs -- in terms of quality of the output -- better than both the Jensen-Shannon and the Symmetrized Kullback-Leibler divergence between histograms. We study the relative gain in quality within the task of unsupervised cue discovery by using a synthetic language corpus.

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited mainly due to the noisiness of user provided tags. To overcome this issue, we propose an approach for characterizing related tags in folksonomies: we use tag co-occurrence statistics and Laplacian score based feature selection in order to create empirical co-occurrence probability distribution for each tag; then we identify related tags on the basis of the dissimilarity between their distributions. For this purpose, we introduce variant of the Jensen-Shannon Divergence, which is more robust to statistical noise. We experimentally evaluate our approach using WordNet and compare it to a common tag-relatedness approach based on the cosine similarity. The results show the effectiveness of our approach and its advantage over the competing method.

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited according to the noisiness of user provided tags. To overcome this problem, we propose an approach for identifying related tags in folksonomies. The approach uses tag co-occurrence statistics and Laplacian score feature selection to create probability distribution for each tag. Consequently, related tags are determined according to the distance between their distributions. In this regards, we propose a distance metric based on Jensen-Shannon Divergence. The new metric named AJSD deals with the noise in the measurements due to statistical fluctuations in tag co-occurrences. We experimentally evaluated our approach using WordNet and compared it to a common tag relatedness approach based on the cosine similarity. The results show the effectiveness of our approach and its advantage over the adversary method.

Folksonomies - collections of user-contributed tags, proved to be efficient in reducing the inherent semantic gap. However, user tags are noisy; thus, they need to be processed before they can be used by further applications. In this paper, we propose an approach for bootstrapping semantics from folksonomy tags. Our goal is to automatically identify semantically related tags. The approach is based on creating probability distribution for each tag based on co-occurrence statistics. Subsequently, the similarity between two tags is determined by the distance between their corresponding probability distributions. For this purpose, we propose an extension for the well-known Jensen-Shannon Divergence. We compared our approach to a widely used method for identifying similar tags based on the cosine measure. The evaluation shows promising results and emphasizes the advantage of our approach.