The ever-growing success of social, tag-based resource sharing systems such as Delicious (bookmarks), Flickr (images), Last.fm (music), YouTube (video), Connotea (bibliographic information) etc shows that in real-life tagging is a very viable solution for annotating any type of resource. Social resource sharing systems are web-based systems that allow users to upload their resources, and to label them with arbitrary keywords, so-called tags. 

In fact, this approach lowers the barrier of metadata annotation, since it requires minimal effort on behalf of annotators: there are no special tools or complex interface that the user needs to get familiar with, and no deep understanding of logic principles or formal semantics required - just some standard technical expertise. This is exactly their greatest strength: intuitiveness.

There are however certain limitations to the unstructured tagging approach:

Let's suppose that user Mary has an account on system S1, that specializes in images. Mary has been using S1 for a while, so she has progressively built a large image collection, as well as a rich vocabulary of tags (personomy).

Another user, Sylvia, which is Mary's friend, is using a different system, S2, to annotate her images. At some point, Mary and Sylvia attended the same event, and each one took some pictures with her own camera. As each user has her reasons for choosing a preferred system, none of them would like to change. They would like however to share the annotation work, where possible: it can be expected that since the pictures were taken at the same time and place, many of them will be annotated in similar way, even by different annotators.

In the course of time Mary also becomes interested in video, starts shooting some of her own and uploads them on system S3. If Mary has both video and photographic material of some event, and since she has already created a personomy on S1, she would naturally like to be able to reuse it on S2 as well. Furthermore, tags could provide a link between potentially related resources, namely those that share the same tag(s). In this case, if a picture and a video share the same tag, they may (within the boundaries of word ambiguity) be about the same topic.

Currently however, achieving the the above tasks is not possible: tags remain confined within each system's boundaries, as there is no interoperability.

Let us imagine that a third person, John, is also using S1. In fact, he is also using a tag that Mary is using, T1, but in a totally different context: as John's preferred language is different than Mary's, they give T1 a different meaning. Nevertheless, navigating S1 would present the items Mary and John have tagged with T1 in the same set.

The above examples demonstrate current limitations of tag-based systems. While tags per se only provide human interpretable semantics it is possible to
establish machine interpretable semantics for tags. Some of the existing efforts to better organize tags include tag bundles (hierarchy of tags), tag clouds (visualization of a set of tags based on their frequency of use) and tag suggestions (showing commonly used tags for resources that have already been tagged by other users). Such mechanisms can be used to derive taxonomic (hypernym and hyponym), synonym, and antonym relations between tags. By relating tags with such semantic relations lightweight ontologies may be built that can be exploited to enhance search and browsing. Basing these lightweight ontologies on Semantic Web standards would also promote interoperability between systems.

One emerging standard that seems to be a natural match for this setting is SKOS. Its intended use is to be leverage expression of thesauri, taxonomies and folksonomies in the Semantic Web standards. There are in fact already some results on how this process could be standardised. Applying similar, automated, methods to personomies could transparently produce their SKOS manifestation, which will enable existing platforms to integrate semantic metadata.