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QUDT - Quantities, Units, Dimensions and Data Types Ontologies

September 18, 2016

Ralph Hodgson, TopQuadrant, Inc.
Jack Spivak, TopQuadrant, Inc.
Steve Ray, CMU, NASA AMES Research Park
Jack Hodges, Web of Systems Group, Siemens Research Technology Center
Paul J. Keller, NASA AMES Research Center


The QUDT Ontologies, and derived XML Vocabularies, are being developed by TopQuadrant and NASA. Originally, they were developed for the NASA Exploration Initiatives Ontology Models (NExIOM) project, a Constellation Program initiative at the AMES Research Center (ARC). They are the basis of the NASA QUDT Handbook to be published by NASA. A presentation on QUDT can be found at


  1. About this Document
  2. Status
  3. QUDT Goals
  4. Overview
  5. Naming and Design Rules
  6. Acknowledgements
  7. References

1: About this Document

This document provides an overview of QUDT, ontologies for "Quantities, Units of measure, Dimensions, and data Types". UML-like diagrams have also been generated to illustrate some key concepts. QUDT models are governed by a catalog, the link to which is provided in the Status Section

The document was generated from QUDT models using SPARQL Web Pages (SWP)

2: Status

Version 1.1 of the QUDT ontologies may be downloaded from the QUDT Catalog, which can also be accessed from Release 2 of QUDT can be accessed from the catalog page at QUDT Release 2 Catalog. The catalog covers many domains of units and quatntities and is being published incrementally.

Currently work is being done on QName construction rules so that all units of measure follow a consistent policy.

3: QUDT Goals

The goals of QUDT are to provide:

  1. A standardized consistent vocabulary, focused on terminology used in science and engineering.
    1. The vocabulary in this standard consists of standardized terminology, definitions, identifiers, and information models.
    2. The intent is to use this vocabulary with a variety of encodings, formats, and data definitions, so it is defined independent of those forms.
    3. Some or all portions of this vocabulary will be of interest to various users and applications, depending on the use case and policy mandates.
    4. It is expected that a large set of existing corpus will not be changed, and so this standard serves as a critical “Rosetta Stone” to reference existing uses of quantities, units, dimensions, and types to a consistent base.
  2. A set of consistent coded identifiers, for human and machine use.
    1. In the same way that modern digital computers could not represent and process meaningful information without the use of standards such as ASCII and Unicode, this standard also introduces a similar coding scheme, for a like purpose.
    2. Assigning an explicit designator (e.g. ASCII uses a numerical value for each letter of the alphabet, numbers, and punctuation) to quantities, units, dimensions, and types is used to provide a robust, unequivocal method of identification of digital information by computer software and hardware.
    3. This definitional approach provides generalized usability for both humans and machines, avoiding problems with uncertainty and misinterpretation.
  3. A collection of foundational vocabularies that can serve a variety of applications. Some examples include:
    1. providing terminology and vocabulary definitions for Documents and Publications. Define consistent terminology for general Program and Project documentation, technical reports, conference papers, guides, drawings, technical specifications, engineering and process documentation, etc.
    2. defining software code documentation, pragmas and/or comments, and independent reference documentation. Referencing system and software variables and constants provides explicit, unambiguous definitions that can be used for data exchanges, semantic consistency, automated checking, software reuse, and more robust search and discovery.
    3. improving the quality of software interfaces, web services, and data exchanges. The model basis of this standard can be used by other software, or to build software, for a variety of purposes; model creation, validation, compilation and run-time checking, translation and transformation, data exchange definitions, etc.
    4. generating schema specifications and data definitions in other formalisms. Examples include database, data file (ex, XML) schema, software application data structures, code-lists, and other controlled vocabularies.
    5. enabling files, datasets, messages, communications and Data Exchange Packages to use consistent terms and constructs when defining elements of datasets and messages in a variety of forms and formats.
  4. A framework designed for extensibility and evolution, but model-based (instead of just a typical dictionary) and governed.
    1. The authors recognize that any given release will not have every possible quantity, unit, dimension, or type that a user may need.
    2. The framework has been designed to grow in a consistent manner.

4: Overview

The QUDT Specification is more than a list of quantities, units, dimensions, data types, enumerations, and structures. In order to provide for interoperability and data exchange between information systems, the specification needs to be available in a machine processable form, with no ambiguities. For these reasons, the QUDT approach to specifying quantities, units, dimensions, data types, enumerations, and other data structures is to use precise semantically grounded specifications in an ontology model with translation into machine-processable representations.

Ontologies provide the object-oriented strengths of encapsulation, inheritance, and polymorphism, strengths which are unavailable in other structured modeling approaches. The characteristics modeled in QUDT require a model-based approach because they are functionally dependent. Modeling one without modeling its dependency on the other requires that the understanding of those dependencies be carried by the observer, which injects ambiguity into the modeling approach. These models (dimensions, coordinate systems, etc.), like everything else, are hierarchical, so using a language to model them which doesn't support inheritance imposes constraints on the models and their use which, again, results in ambiguity.

QUDT supports system interoperability in four ways:

  1. The unit ontologies provide a formal way of specifying units explicitly, thereby avoiding tacit conventions that are prone to misinterpretation.
  2. QUDT distinguishes between variants of a given unit. For example, the English word "day" interpreted as a unit of measure may refer to a mean solar day, a sidereal day, or the length of time equivalent to exactly 86,400 seconds. Each of these interpretations of "day" appears as a distinct unit in the ontology.
  3. QUDT distinguishes between units of different types that are commonly referred to with the same name. For example, "second" may refer to a measure of time or a measure of angle. Again, each usage appears as a distinct term in the ontology.
  4. The ontology provides explicit conversion information, serving as a single point of reference for such conversions.

QUDT is based largely on the international standard for metric units (SI), as described in "BIPM International System of Units", the "ISO standards on Units and Quantities", and "The NIST Guide for the use of the International System of Units". In addition, QUDT includes units from other systems, such as CGS units for mechanics, CGS EMU (electromagnetic) units, CGS-ESU (electrostatic) units, and Gaussian units for electrodynamics, and the Planck system of natural units. Most US Customary and British Imperial units for length, weight, and heat are also included.

Wherever applicable, the SI standard is used for conversions between non-SI units. To convert from unit U1 to U2, one first converts U1 to SI (the equivalent value in the appropriate SI unit), then converts SI to U2. In the QUDT Ontologies, each unit has a corresponding conversion multiplier, which multiplied to quantities to convert from the current unit to the corresponding SI unit. So, if N1 and N2 are the conversion multipliers for U1 and U2 respectively, then the proper factor to convert from U1 to U2 is N1/N2. Unit conversion data was largely derived from the values given by the National Institute of Standards and Technology (NIST) for fundamental constants, as documented in "The NIST Reference on Constants, Units, and Uncertainty".

QUDT semantics are based on dimensional analysis expressed in the OWL Web Ontology Language (OWL). The dimensional approach relates each unit to a system of base units using numeric factors and a vector of exponents defined over a set of fundamental dimensions. In this way, the role of each base unit in the derived unit is precisely defined. A further relationship establishes the semantics of units and quantity kinds. By this means, QUDT supports reasoning over quantities as well as units. QUDT models may be translated into other representations for machine processing, or other programming language structures according to need. The following sections briefly define the primary objects of interest in the QUDT ontology and their relevance to the formal specification of quantities, units, dimensions and data types.

4.1: Ontology Architecture


4.2: Quantity Kind

A Quantity Kind is any observable property that can be measured and quantified numerically. Familiar examples include physical properties such as length, mass, time, force, energy, power, electric charge, etc. Less familiar examples include currency, interest rate, price to earning ratio, and information capacity.


4.3: Quantity

A quantity is the measurement of an observable property of a particular object, event, or physical system. A quantity is always associated with the context of measurement (i.e. the thing measured, the measured value, the accuracy of measurement, etc.) whereas the underlying quantity kind is independent of any particular measurement. Thus, length is a quantity kind while the height of a rocket is a specific quantity of length; its magnitude that may be expressed in meters, feet, inches, etc. Examples of physical quantities include physical constants, such as the speed of light in a vacuum, Planck's constant, the electric permittivity of free space, and the fine structure constant.

In other words, quantities are quantifiable aspects of the world, such as time, distance, velocity, mass, momentum, energy, and weight, and units are used to describe their measure. Many of which are related to each other by various physical laws, and as a result the units of some of the quantities can be expressed as products (or ratios) of powers of other units (e.g., momentum is mass times velocity and velocity is measured in distance divided by time). These relationships are discussed in dimensional analysis. Those that cannot be so expressed can be regarded as "fundamental" in this sense.

A quantity is distinguished from a "quantity kind" in that the former carries a value and the latter is a type specifier.


4.4: Quantity value

A Quantity Value expresses the magnitude and kind of a quantity and is given by the product of a numerical value n and a unit of measure U. The number multiplying the unit is referred to as the numerical value of the quantity expressed in that unit. Refer to NIST SP 811 section 7 for more on quantity values.


4.5: Quantity Dimension Vector

A Quantity Dimension Vector is a relationship between a quantity system, a quantity kind of that system, and one or more dimension vectors. The dimensions of a quantity are expressed as a product of the basic physical dimensions mass, length, time, electric charge, and absolute temperature as \(dim \, Q = L^{\alpha} \, M^{\beta} \, T^{\gamma} \, I ^{\delta} \, \theta ^{\epsilon} \, N^{\eta} \, J ^{\nu}\), where the rational powers, named dimensional exponents, \(\alpha, \, \beta, \, \gamma, \, \delta, \, \epsilon, \, \eta, \, \nu\), are positive, negative, or zero.

For example, the dimension of the physical quantity \(\it{speed}\) is \(\boxed{length/time}\), \(L/T\) or \(LT^{-1}\), and the dimension of the physical quantity force is \(\boxed{mass \times acceleration}\) or \(\boxed{mass \times (length/time)/time}\), \(ML/T^2\) or \(MLT^{-2}\) respectively.


4.6: Unit

A unit of measure, or unit, is a particular quantity value that has been chosen as a scale for measuring other quantities the same kind (more generally of equivalent dimension). For example, the meter is a quantity of length that has been rigorously defined and standardized by the BIPM (International Board of Weights and Measures). Any measurement of the length can be expressed as a number multiplied by the unit meter. More formally, the value of a physical quantity Q with respect to a unit (U) is expressed as the scalar multiple of a real number (n) and U, as \(Q = nU\).


5: Naming and Design Rules

[TBD: document the NDR]

[TBD: document the compliance and extensions to NIST SP 800]

6: Acknowledgements

  1. NASA AMES Research Center for sponsoring and content for different engineering disciplines
  2. TopQuadrant, Inc., for Ontology Architecture, foundation ontologies and tooling support
  3. European Space Agency (ESA), for constructive dialog and input to the ontology models
  4. Siemens Research Labs
  5. openPHACTS

7: References

  1. The NIST Guide for the use of the International System of Units
  2. International Vocabulary of Metrology – Basic and General Concepts and Associated Terms
  3. SI Brochure, 8th Edition
  4. Dimensional Analysis, Percy Williams Bridgman, Yale University Press (1922)

Last Updated September 18, 2016

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