continued taxonomy/ontology

This is a continuation of the first project. _read the deliverable for details _ ## Deliverables 1) Complete and fully-functional working program(s) in executable form as well as complete source code of all work done. 2) Deliverables must be in ready-to-run condition, as follows (depending on the nature of the deliverables): a) For web sites or other server-side deliverables intended to only ever exist in one place in the Buyer's environment--Deliverables must be installed by the Seller in ready-to-run condition in the Buyer's environment. b) For all others including desktop software or software the buyer intends to distribute: A software installation package that will install the software in ready-to-run condition on the platform(s) specified in this bid request. 3) All deliverables will be considered "work made for hire" under U.S. Copyright law. Buyer will receive exclusive and complete copyrights to all work purchased. (No GPL, GNU, 3rd party components, etc. unless all copyright ramifications are explained AND AGREED TO by the buyer on the site per the coder's Seller Legal Agreement). What I will be doing is taking our taxonomy that we developed..along with the integration of open available ontologies and meta data systems to create an automated classification and retrieval semantic system for another application I am working on. This design we are working on will be taken into consideration. Along with the other things I have already listed. You should do searches online for other available open ontology and metadata systems like [login to view URL] We are trying to create the basic entity for solving some of the logistical problems of semantic integration and [login to view URL] problem or data set has a few nearest neighbors whic determine context, these association and the concept of context are inherently probablistic in nature "associative" and not hierarchical. The trick is an associative heuristic first followed by a probabalistic "directive/action". This is linguistics. Use the associative heuristic to assume place and then a probablistic rule set of actions to assume likely verb. Then return provide a series of option for the users. Would you like me to "insert verb" to "insert noun" a very simple functional vocabulary eminating from probablistic context. So we need a top level structure for sorting the data for instances like automated entry. However, we need a buttom up for labeling data and creating data within this structure almost like a wikipedia but it has to be based on semantic principles. basically it has to be able to draw the context of the relationships between the taxonomy, ontologies, etc systems...for instance can automatically categorize data with no requirement for manual input whatsoever. The flexibility of categorization features allows you to precisely derive categories using concepts found within unstructured text. This ensures that all data is classified in the correct context with the utmost accuracy [[login to view URL] _[login to view URL]][1] [login to view URL] [[login to view URL] [login to view URL]][2] <[login to view URL]> <[login to view URL]> Definately read this: Taking special of topdown buttom up approaches <[login to view URL]> ## Platform design for multi platform