![](bg8.png)
have been assigned to a group and tags of group mem-
bers. In (Rattenbury et al., 2007) the authors inves-
tigated how to learn more concrete semantics from
folksonomies. In particular, they presented an ap-
proach to distinguish between event tags and place
tags. Mentioned approaches for learning semantics
can also be applied to GroupMe!. At the moment, in-
stead of learning vague semantics, GroupMe! extracts
semantic descriptions explicitly when new resources
are added to a group. Hence, these descriptions can
be utilized by machines offhand in order to search for
certain type of resources (cf. example in Section 2).
Therefore, all RDF produced in GroupMe! is feeded
back to the Web via RDF feeds and RESTful API.
Other systems like CiteULike
20
or BibSonomy just of-
fer RSS export.
5 CONCLUSIONS
GroupMe! gives users the possibility to group Web
resources in an easy way – by simple drag & drop
operations – and combines this idea with features of
social tagging systems. The evaluation of GroupMe!
shows that users appreciate the grouping facility to or-
ganize Web resources they are interested in. Groups
can be seen as hand selected collections of Web con-
tent for a certain topic or domain. As such, they are
also valuable results to search queries, and our inves-
tigations have shown that users recognize this and se-
lect groups among the search results often.
The structure inherently given by the groups can also
be used to infer information about the content of Web
resources. This is interesting for non-tagged Web
resources, and particularly for multimedia Web re-
sources whose content is - without tags - hardly deter-
minable (like videos etc.). The analysis of the search
behavior of users has revealed that this exploitation
of grouping information uncovers relevant content,
which – with tagging alone – would not have been
found.
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A NOVEL APPROACH TO SOCIAL TAGGING: GROUPME! - Enhancing Social Tagging Systems with Groups
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