Welcome to the human-readable and machine-readable knowledge graph for PROOF.
This free open source resource enables software engineers to understand how to create supercharged software applications related to PROOF or other financial reporting schemes represented using XBRL. It is based on a proven, best-practices, global standard method.
PROOF Knowledge Graph
This XBRL-based machine-readable information essentially forms a knowledge graph. The PROOF Knowledge Graph is a set of terms, structures, assocations, and rules that are used to build models and report facts per those models.
The following is a quick reference to information provided within this human-readable and machine-readable knowledge graph.
- XBRL Syntax Rules: (Human | Machine)
- Model Structure Rules: (Human | Machine)
- Fundamental Accounting Concept Rules: (Human | Machine)
- Disclosure Mechanics Rules: (Human | Machine)
- Reporting Checklist Rules: (Human | Machine)
- Type/Subtype Rules: (Human | Machine)
- Disclosures: (Human | Machine) The financial reporting scheme requires specific disclosures.
- Topics: (Human | Machine) Those disclosures can be organized within specific topics.
- Templates: (Human | Machine) Templates provide examples of those disclosures.
- Exemplars: (Human | Machine) Examples (exemplars) exist in the form of how other economic entities reported these same disclosures.
- Terms: (Human | Machine) Terms are used to represent disclosures in a machine-readable XBRL taxonomy.
- Structures: (Human | Machine) Reported disclosures are represented using XBRL networks, XBRL hypercubes, and logical blocks which form a model.
- Fundamental Concepts: (Human | Machine) High-level fundamental accounting concepts exist within the conceptual framework for PROOF
- Reporting Styles: (Human | Machine) Reporting styles are models of relations between fundamental accounting concepts that form an approach to creating a report.
- Consistency Rules: (Human | Machine) Consistency rules explain the relations between fundamental accounting concepts.
- Derivation Rules: (Human | Machine) When a fundamental accounting concept is not explicitly reported; derivation (a.k.a. impute) rules are used to use logical deduction to derive the value of the unreported high-level financial concept.
- Facts: (Human | Machine) Facts must be consistent with the permitted terms, structures, associations, and rules. When the statements and facts are consistent then the logical system is functioning properly (consistent, complete, precise).
The following is important basic technical information that will help get software engineers started. For a good book see The XBRL Book: Simple, Precise, Techical.
Information Helpful in Using Theory, Framework, and Method
The following information is helpful in understanding and using the theory, framework, and method that drive XBRL-based digital financial reporting:
Pacioli Toolkit/Platform and Tool to Scrutinize XBRL-based Digital Financial Report
Pacioli is a logic toolkit/platform for working with XBRL-based digital fiancial reports. Pacioli is a web service constructed using SWI Prolog, leverages my theory, framework, and method for interacting with XBRL-based financial reports.
Last updated: 3/23/2021 5:34:35 PM