Requirements to Support a Modern 

Architecture for Industrial Data and 

Industrial Data Standards on the 

Semantic Web

Evan Wallace

David Leal

Allison Barnard Feeney

NISTIR 7729
NISTIR 7729

Requirements to Support a Modern 

Architecture for Industrial Data and 

Industrial Data Standards on the 

Semantic Web

Evan Wallace

Allison Barnard Feeney

Manufacturing Systems Integration Division

Engineering Laboratory

David Leal

CAESAR Systems Limited

September 2010

U.S. Department of Commerce

Gary Locke, Secretary

National Institute of Standards and Technology

Patrick D. Gallagher, Director
Requirements to support a modern architecture for 

industrial data and industrial data standards on the 

semantic web 

Evan Wallace, National Institute of Standards and Technology, ewallace@nist.gov

David Leal, CAESAR Systems Limited, david.leal@caesarsystems.co.uk

Allison Barnard Feeney, National Institute of Standards and Technology, abf@nist.gov 

Introduction: The subcommittee of the International Organization for Standardization 

(ISO) tasked with developing standards for industrial data, TC 184/SC 4, has recently 

undertaken an effort to define a new, more flexible architecture based on widely-used 

information technologies and standards. The goal of this effort is to reduce barriers to 

the use of SC 4 standards by industry and increase the utility of SC 4 standards beyond 

data exchange. This paper provides an overview of the proposed architecture, the types 

of data it must support and the languages/technologies chosen to support it, and the 

challenges that it may impose on the use of the Resource Description Framework (RDF) 

for implementing this architecture. These challenges are similar to those experienced in 

other uses of RDF, and with this report we hope to encourage further enhancements in 

standards and tools for RDF to meet these shared needs. 

Background: 

While the nominal area of interest of ISO TC184/SC4 is Industrial Data, its official scope 

of work is described as follows: 

Standardization of the definition, integration and quality management of information 

which is shared, exchanged and archived in the context of: 

• the definition, description and classification of products and facilities throughout 

their lifecycle 

• industrial management and operations 

• other information requirements supporting automation systems

The focus early in SC 4 was on the exchange of product data, and the tools and 

architecture developed at the time reflect that purpose. Principal among these was a 

data modeling language called EXPRESS[1],[2] developed specifically to support SC 4 

information standards. EXPRESS was, and is, a quite expressive data modeling 

language with a powerful constraint capability. The SC 4 architecture decoupled the 

model from the implementation technology, enabling reuse of EXPRESS models with 

different encoding methods or data sharing techniques. SC 4 is now a rich source of 

consensus-based, domain-specific information models; however, there are barriers to 

SC 4 standards use by and utility to industry. 

• EXPRESS has not gained wide acceptance outside of the community associated 

with SC 4 standards 

• SC 4’s focus on data exchange means SC 4 models aren’t part of a broader 

information technology architecture that supports all of the information needs of 

an enterprise 

A new architecture: 

Recognizing the need to adapt to the changing information technology landscape, SC 4 

formed a project to propose a future SC 4 architecture. The stated objectives of the 
architecture are to 1) reduce greatly the barriers to the use of SC 4 standards by 

industry, 2) reduce barriers to the use of SC 4 standards in conjunction with standards 

from other bodies such as the Object Management Group (OMG) and the Organization 

for the Advancement of Structured Information Standards (OASIS), and 3) increase the 

utility of SC 4 standards to industry [3]. 

The design drivers for this project can be summarized by the following: 

Provide SC 4 with a framework/architecture enabling its projects to create standards 

using the same technologies, methodologies and approaches that are the best 

practice in industry and in other standards-making bodies. 

The evolving architecture, referred to as Industrial Data Integrated Ontologies and 

Models (IDIOM), consists of three parts: 1) an information technology (IT) framework of 

software technologies and tools; 2) methodologies, policies, and guidelines for the use of 

the tools; and, 3) a framework of core concepts. Most effort to date has focused on the 

IT Framework as it is the basis for the other parts. 

Ontology Summit 2009, on the topic “Toward Ontology-based Standards” recommended 

the use of ontologies as the best practice for specifying information standards [4]. 

Accordingly, IDIOM has placed ontologies specified using logic-based languages at the 

center of its approach. This makes the Web Ontology Language (OWL) and RDF 

primary candidates for supporting parts of the IT Framework of this architecture. 

Components of the IT framework include 

• natural language dictionaries 

• ontologies 

• process models 

• data models 
• service models 

• mappings and traces 

The components of the IT Framework include models at varying levels of abstraction. 

Some technologies are used to express more abstract models and others declare 

elements that are directly implementable. The Future SC 4 Architecture project 

surveyed ISO, OMG, OASIS and other bodies that are standardizing process models, 

services models and data models. The IDIOM approach was informed by the following 

findings 

• When industry organizations and standards bodies develop ontologies, the standard 

language used more than any other ontology language is OWL. 

• When industry organizations and standards bodies develop process models, the 

standard languages used more than any other process modeling languages are the 

Unified Modeling Language (UML) and Business Process Modeling Notations 

(BPMN). 

• When industry organizations and standards bodies develop service models, the 

standard languages used more than any other service modeling language are UML, 

a profile of UML corresponding to the Web Service Definition Language (WSDL), 

and/or WSDL itself. 

• When industry organizations and standards bodies develop data models, the 

standard languages used more than any other data modeling language are UML, 

EXPRESS (primarily in SC 4), and XML Schema. 

The goal of IDIOM is to document industry best practices for industrial data. While the 

draft specification of IDIOM names a set of technologies for each component, best 

practices change. IDIOM is intended to be flexible and to be extended as new 

technologies become available. 

The brief description above of the proposed new architecture may suggest to the reader 

that it must support quite a variety of types and formats for information. This is quite true. 

But the demands on the IDIOM framework are even greater since it is intended to 

support the model and data sharing needs not only of enterprises using SC 4 standards 

but also of those developing and disseminating the standards. 

Demands on RDF to support IDIOM: 

The web-based nature of RDF, its flexibility, its tool support, and its formality make it a 

desirable foundation for IDIOM. The primary challenges that we see to using Semantic 

Web languages for the IDIOM IT framework are 

1. the expressiveness needed for its diverse content 

2. the different semantics that must apply to different types of content 

The latter point covers two different categories of concern. The semantics of the 

language needs to support notions for instance of time or change (for process or service 

models), as well as to close worlds such that completeness or other integrity constraints 

could be specified for classes of data or individual data sets (this is a typical 

interpretation of data models). The language also needs to provide the necessary 

context for interpretation to qualify a truth statement. Two common kinds of product data 

where this second concern would apply are measurements and simulation results. The 

meaning of such data can't be understood without either more information about the 

measurement or simulation (such as when, where, how, and under what conditions it 
was generated and by whom), or some claim made by some agent about what can be 

inferred from this measurement and its context. 

We believe that making this claim should be possible with Named RDF Graphs while the 

Proof Markup Language (PML)[5] may provide a means of documenting a justification 

for the claim. 

The use of the IT framework for standards development and for product design/lifecycle 

support will also require similar annotations to make claims regarding the status of 

standards or product designs with respect to standard workflows. Here again Named 

RDF Graphs may be useful for identifying the content having the status, and PML may 

be usable for describing how it came to that state (much as described in [6]). 

Examples: 

The following are some plain text examples illustrating some of the kinds of information 

that would be captured using IDIOM that may push the boundaries of standards or 

recommended practices for RDF. 

Measurement: Temperature sensor TS_101 records that stream F_567-at-2010-04-

05T11:36 has a temperature of 410 deg C. 

Simulation results: On the basis of finite element analysis A (to find the peak stresses), 

scenario definition D (to specify the number of cycles), and material property 

specification M (defining the high cycle fatigue S-N curve) it is predicted that part P_101 

in role R_101 will fail after 100,000 hours. 

Engineering design approval/status: Process design information PD_E_101 about unit 

U_101 is approved for release to Fred Bloggs and Co. as the basis for mechanical 

engineering design. 

Reference data status: The thing assigned the URI <http://standards.iso.org/iso/12345/-

6/tech/widget> is defined by the English language text "A thing that is a blabla ..."; has 

the English language label "Widget"; has the French language label "Blabla"; and has 

ISO stage code 60.60 (i.e. is published as IS). 

Summary: 

It is important for standards and data related to the design and production of 

manufactured products (i.e. industrial data) to be clear, correct, and unambiguously 

interpreted. Misinterpretation of this information could lead to production problems, user 

dissatisfaction, unnecessary expenses, or even injury or loss of life. The formality of 

logic languages offer capabilities to address this need previously unavailable in 

standards-based information technologies. The IDIOM framework recommends the use 

of RDF and OWL to capture, relate, and describe this information because of their formal 

underpinning as well as their pragmatic features for web use. But the expressivity 

limitations and interpretation as truth statements that come with this formality create new 

challenges when applied to the richness and diversity of industrial data. 

We ask that the World Wide Web Consortium (W3C) consider these challenges while it 

evaluates if and how RDF should evolve. This may be key in determining the level of 

use of RDF in the development of standards, models, and data for the industrial domain, 
as well as for domains with similar characteristics such as electric grid management and 

building construction. 

Specific things that we would like to see from the W3C and the RDF community are 

• guidance on how to use RDF in a coherent manner to describe and manage 

information in a heterogeneous environment that mixes content captured as RDF 

with content in other modeling languages such as EXPRESS, UML, and BPMN 

• maturation of RDF named graphs to Recommendation 

• maturation of a standard way to express workflows that describe the meaning of 

status metadata associated with an RDF graph 

• guidance on the use of RDF and RDF Named Graphs to represent data with 

associated provenance metadata 

• guidance on the use of RDF and RDF Named Graphs to represent data with 

associated metadata for expressing context such as for expressing measurement 

information 

• guidance and potentially standardized vocabularies to express beliefs, and to 

relate those beliefs to the graphs that motivate them 

References: 

1. ISO 10303-11:2004, Industrial automation systems and integration – Product 

data representation and exchange – Part 11: Description methods: The 

EXPRESS language reference manual. 

2. Wikipedia entry for 

EXPRESS http://en.wikipedia.org/wiki/EXPRESS_(data_modeling_language) 

3. SC 4 Future Architecture Wiki, http://futurearch.wikispaces.com

4. Ontology Summit 2009 Communiqué: Toward Ontology-based Standards, 

version 1.1.1, http://ontolog.cim3.net/cgibin/wiki.pl?OntologySummit2009_Communique#nid1WLI

5. Proof Markup Language (PML) Primer, http://iw.stanford.edu/2005/wd-pmlprimer/pml-primer-20051128.htm

6. James R. Michaelis, Li Ding, Zhenning Shangguan, Stephan Zednik, Rui Huang, 

Paulo Pinheiro da Silva, Nicholas Del Rio, Deborah L. McGuinness: Towards 

Usable and Interoperable Workflow Provenance: Empirical Case Studies Using 

PML, In Proceedings of the First International Workshop on the Role of Semantic 

Web in Provenance Management