The first version of a manual for the bio-zen ontology is now available for download. Both the manual and the ontology in OWL format can be downloaded from http://neuroscientific.net/index.php?id=download
>From the introduction: "The development of the bio-zen ontology framework is an attempt to represent data, information and knowledge from research in all facets of the life sciences on the Semantic Web. The goal of this project is the unification of information that is now scattered through a multitude of different data structures, exchange formats and databases. Through the use of Semantic Web technologies, the decentralised and barrier-free development and exchange of experimental data, hypotheses and biological models becomes possible. Conventional databases (e.g. relational databases or XML databases) do a poor job of representing biological reality. Researchers that want to publish or search for information do not only have to know about the biological structures they are investigating, they also have to deal with the structures of the database tables, file formats or XML documents - all of which are in most cases only remotely similar to the mental representation we have in mind when thinking about biological facts. Furthermore, most databases in use nowadays were designed with only a small and limited field of investigation in mind, and so we are now confronted with a convolute of small databases that can only be made to work together through a lot of additional work. We also see that systems biology with its focus on the simulation of complex biological systems has an ever growing impact on classical molecular biology. However, the realm of qualitative information that is represented in texts and databases like Uniprot or BIND is completely disconnected from the world of simulation and modelling, which is currently represented with languages like SBML, CellML or NEURON models. To realize the promises of systems biology, the division between these two worlds has to be bridged. The bio-zen framework uses Semantic Web technologies to overcome the limitations of current information systems in the life sciences. The descriptions of biological reality in the bio-zen framework are very similar to the cognitive models researchers have about their subjects of investigation, making the work with information systems more intuitive for the individual scientist. bio-zen is exceptionally flexible and extensible, making it easy to represent information from a wide variety of fields in a common framework. It also allows for a seamless integration of mathematical descriptions and simulation parameters into qualitative information, enabling a quick transition from data and information to model simulations and back. Bio-zen is designed to be very agile and open for collaborative participation and extension of information bases while retaining full logical consistency. It allows for the distributed creation of uncontrolled vocabularies (so-called folksonomies ). In contrast to controlled vocabularies, such folksonomies are open-ended and can therefore respond quickly to changes and innovations in the way researchers categorize their observations. The philosophy behind this loosely controlled, collaborative annotation is similar to that of other peer production systems such as Wikipedia or Nature's Connotea . Although the participating individuals possess varying levels of tagging sophistication, such a production process can produce results that compare favourably to professionally curated, centralised systems. If successful, the development of Semantic Web infrastructure could mark the beginning of a whole new paradigm in the organisation and dissemination of information in the life sciences." If you have anything to discuss that cannot be discussed on this mailing list, write me an e-mail or use the discussion forum on the website (please give your full name). If you find any mistakes and errors (including grammatical mistakes...), I would also welcome a message from you. kind regards, Matthias Samwald ------------------------------------------------------------------------ Matthias Samwald, Section on Medical Expert and Knowledge-Based Systems Core Unit for Medical Statistics and Informatics Medical University of Vienna -- Konrad Lorenz Institute for Evolution and Cognition Research University of Vienna -- neuroscientific.net
