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JSONiq primarily provides means to extract and transform data from JSON documents or any data source that can be viewed as JSON (e.g. relational databases or web services). The major expression for performing such operations is the SQL-like “FLWOR expression” that comes from XQuery. A FLWOR expression is constructed from the five clauses ...
The W3C's XSL-FO (XSL Formatting Objects) markup language is commonly used to generate PDF files Metadata , data about data, can be used to organize electronic resources, provide digital identification, and support the archiving and preservation of resources.
JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. It provides a contract for the JSON data required by a given application and how that data can be modified. [29] JSON Schema is based on the concepts from XML Schema (XSD) but is JSON-based. As in XSD, the same ...
JSON Pointer [10] defines a string syntax for identifying a single value within a given JSON value of known structure. JSONiq [11] is a query and transformation language for JSON. XPath 3.1 [12] is an expression language that allows the processing of values conforming to the XDM [13] data model. The version 3.1 of XPath supports JSON as well as ...
PDFCreator is an application for converting documents into Portable Document Format format on Microsoft Windows operating systems. It works by creating a virtual printer that prints to PDF files, and thereby allows practically any application to create PDF files by choosing to print from within the application and then printing to the PDFCreator printer.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
Machine-readable data must be structured data. [1]Attempts to create machine-readable data occurred as early as the 1960s. At the same time that seminal developments in machine-reading and natural-language processing were releasing (like Weizenbaum's ELIZA), people were anticipating the success of machine-readable functionality and attempting to create machine-readable documents.
Use the API to fetch data in XML or JSON packaging The backup script dumpBackup.php dumps all the wiki pages into an XML file. dumpBackup.php only works on MediaWiki 1.5 or newer.