A Folksonomy Ranking Framework: A Semantic Graph-based Approach*
Hyun-Jung Park, Sangkyu Rho · 2011
In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm w ould assign higher ranking to more useful resources or experts. What resources are considered useful in a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with more expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are PageRank by Google and HITS(H ypertext Induced Topic Selection) by Kleinberg. Both PageRank and HITS assign a higher evaluation sc ore to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable. In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual interactions between entities, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, propos ed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative