Ontologies and tag-statistics
Gergely Tibely, Peter Pollner, Tamas Vicsek, Gergely Palla

TL;DR
This paper investigates how hierarchical structures of tags in DAGs influence their statistical distribution and co-occurrence in real networks, revealing local relevance outweighs global position, and proposes a random walk model to replicate these features.
Contribution
It provides an analysis of tag distributions in DAG-organized tags and introduces a simple random walk model to simulate tag co-occurrence statistics.
Findings
Local relevance of tags is more significant than their global distance from the root.
Tag co-occurrence patterns can be reproduced by a random walk model on the DAG.
Hierarchical position influences tag statistics more than overall hierarchy depth.
Abstract
Due to the increasing popularity of collaborative tagging systems, the research on tagged networks, hypergraphs, ontologies, folksonomies and other related concepts is becoming an important interdisciplinary topic with great actuality and relevance for practical applications. In most collaborative tagging systems the tagging by the users is completely "flat", while in some cases they are allowed to define a shallow hierarchy for their own tags. However, usually no overall hierarchical organisation of the tags is given, and one of the interesting challenges of this area is to provide an algorithm generating the ontology of the tags from the available data. In contrast, there are also other type of tagged networks available for research, where the tags are already organised into a directed acyclic graph (DAG), encapsulating the "is a sub-category of" type of hierarchy between each other.…
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