Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.

The article discusses ways to let semantics emerge from simple observations from the bottom-up, rather than imposing concepts on the observations top-down, to provide precise query, retrieval, communication or translation for a wide variety of applications. The following areas are examined: image retrieval and databases; media information spaces including the Semantic Web and MPEG frameworks; language games for emergent semantics; and emergent semantics for ontologies