The quality of the current tagging services can be greatly improved if the service is able to cluster tags by their meaning. Tag clouds clustered by higher level topics enable the users to explore their tag space, which is especially needed when tag clouds become large. We demonstrate TagCluster – a tool for automated tag clustering that harnesses knowledge from Wikipedia about semantic relatedness between tags and names of categories to achieve smart clustering. Our approach shows much better quality of clusters compared to the existing techniques that rely on tag co-occurrence analysis in the tagging service.
Social media sharing sites such as Flickr or YouTube have become immensely popular. Besides sharing actual content, users also share annotations describing or classifying the contents they publish. Although tagging is easy, annotation still is a laborious task that can be made easier by suggesting meaningful additional tags to the user automatically. In this position paper we propose a system architecture and process for supporting annotation by tag suggestion to increase the quality and quantity of social annotations. The goal is not to tag previously untagged images in a completely automatic way, but instead to extend the amount and completeness of annotations by supporting the user in the process of adding further tags.
In this position paper we present a vision of how the stories that people tell in Internet weblogs can be used directly for automated commonsense reasoning, specifically to support the core envisionment functions of event prediction, explanation, and imagination.
In this contribution Wikis are interpreted as social information spaces. These information spaces can be decomposed in different networks. Here, one network is introduced – the collaboration network. This network type exemplifies how dynamics in social information spaces can be analyzed. For this, different approaches of visualizing networks are explained. The chosen approach is applied in an descriptive study. The open community project Wikiversity is examined to introduce one possible analysis in SONIVIS:Tool – an open source ntwork mining software.
A mining method for egocentric and polycentric queries in multi-dimensional networks is proposed. The method allows fast search for objects in sufficient proximity of other object(s) where the proximity is defined in terms of multiple relationships between objects. The method uses spreading activation technique. Other potential uses of spreading activation technique are also outlined and, in particular, include applications to collaborative filtering (community detection based on tag recommendations, expertise location, etc). Moreover, the spreading activation technique is combined with so-called ambient navigation. The advantages of such approach are high performance and high scalability in terms of size of multidimensional network. The proposed method is very practical and is implemented in IBM LanguageWare software products.