Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge

The participatory nature of many Web 2.0 platforms makes a large portion of users’ interactions with each other and with information resources digitally observable. The assumption that the evolving structure of these digital records contains implicit evidences for the underlying semantics has been proven by successful approaches of making the emergent semantics explicit, e.g. in the form of lightweight ontologies. In this paper, we provide further evidence for the great potential of self-emerging ontologies from Web 2.0 data, exemplified by collaborative tagging systems. We hereby combine and extend prior research, where we identified crucial aspects for successful methods to infer tag semantics. The additional contribution of this paper is to propose an extended methodology to induce a hierarchical organization scheme from the initially flat tag space which captures the semantics and the diversity of the shared knowledge. It comprises the introduction of a synsetized folksonomy (which tackles the problem of synonymous tags) and a clustering approach for tag sense disambiguation. In order to assess the quality of the learned semantics, we compare the inferred organization scheme with manually built categorization schemes from WordNet and Wikipedia. Our results exhibit clear similarities; so in summary, our work demonstrates a successful example of self-emergent ontologies from Web 2.0 data.