> For the complete documentation index, see [llms.txt](https://virtualfactory.gitbook.io/vlft/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://virtualfactory.gitbook.io/vlft/kb.md). # VLF Knowledge Base The VLF Knowledge Base is grounding on a standard, extensible, and common data model for the representation of factory objects related to production systems, resources, processes and products. This [factory data model](/vlft/kb/fdm.md) is developed as an [OWL ontology](https://www.w3.org/OWL/), since this language provides a way to generate a flexible data model integrating different knowledge domains, enabling knowledge sharing between several applications and a fluent flow of data between different entities. In particular, the VLF Knowledge Base exploits already existing technical standards (e.g. [Industry Foundation Classes](https://technical.buildingsmart.org/standards/ifc/), [UML Statechart](https://www.omg.org/spec/UML/About-UML/), [W3C SSN/SOSA](https://www.w3.org/TR/vocab-ssn/)) and research results by CNR-STIIMA and Politecnico di Milano-Mechanical Engineering Department. The VLF Knowledge Base will be continuously extended by adding concepts to the data model that is used to [instantiate models](/vlft/kb/instantiation.md) representing [academic use cases and industrial case studies](/vlft/use-cases.md). The results of the modelling activities will be stored in the knowledge base to be used for future teaching and training purposes. The knowledge base can be implemented adopting different technologies ranging from file-based solutions to relational databases and to native triple stores. **Contact person**: [Walter Terkaj (CNR-STIIMA)](mailto:walter.terkaj@stiima.cnr.it) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://virtualfactory.gitbook.io/vlft/kb.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.