Extracting Tag Hierarchies

Abstract

Tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity. Over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a "flat", egalitarian organization of the tags, which is very common when the tags correspond to free words given by numerous independent people. Here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics. Along with proposing new algorithms, we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects. Furthermore, we set up a synthetic, computer generated benchmark providing a versatile tool for testing, with a couple of tunable parameters capable of generating a wide range of test beds. Beside the computer generated input we also use real data in our studies, including a biological example with a pre-defined hierarchy between the tags. The encouraging similarity between the pre-defined and reconstructed hierarchy, as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications. Tags have become very prevalent nowadays in various online platforms ranging from blogs through scientific publications to protein databases. Furthermore, tagging systems dedicated for voluntary tagging of photos, films, books, etc. with free words are also becoming popular. The emerging large collections of tags associated with different objects are often referred to as folksonomies, highlighting their collaborative origin and the “flat” organization of the tags opposed to traditional hierarchical categorization. Adding a tag hierarchy corresponding to a given folksonomy can very effectively help narrowing or broadening the scope of search. Moreover, recommendation systems could also benefit from a tag hierarchy.

Hierarchical networks of scientific journals

“…A very closely related problem is that given by the automatized categorization of free tags appearing in various online content (Heymann and Garcia-Molina, 2006;Schmitz, 2006;Damme et al, 2007;Plangprasopchok et al, 2011;Tibély et al, 2013;Velardi et al, 2013). In recent years, the voluntary tagging of photos, films, books and so on, with free words has become popular on the Internet in blogs, various file-sharing platforms, online stores and news portals.…”

“…Here we apply a generalized version of a recent tag hierarchy extraction method (Tibély et al, 2013) for constructing a nested hierarchy between scientific journals. In its original form, the input of the tag hierarchy extraction algorithm is given by the weighted co-occurrence network between the tags, where the weights correspond to number of shared objects.…”

“…On the one hand, the flow hierarchy analysis of this network based on the m-reaching centrality (Borgatti, 2003;Mones et al, 2012) reveals the structure relevant from the point of view of knowledge spreading and influence. On the other hand, the alternative hierarchy obtained from the same network with the help of an automated tag hierarchy extraction method (Tibély et al, 2013) highlights a nested structure with the most interdisciplinary journals at the top and the very specialized journals at the bottom of the hierarchy.…”

A supervised learning approach to detect subsumption relations between tags in folksonomies

“…An unweighted graph is constructed in which edges link pair of tags if the similarity is greater than an estimated threshold. Tibely et al [14] use tag cooccurrence statistics for tag hierarchy extraction rooted on complex network theory, that extract a pruned direct acyclic graph between tags from a weighted co-occurrence network, where the weight of an edge corresponds to the number of shared resources.…”

“…To overcome this problem, many research works propose to construct hierarchies of tags automatically by means of heuristic algorithms [4,11,14]. In this paper, we propose a more principled approach where the subsumption relations are learned directly from folksonomy data.…”