NISTIR 7965

A Methodology for Handling 

Standards Terminology for 

Sustainable Manufacturing

Anantha Narayanan

David Lechevalier

KC Morris

Sudarsan Rachuri 
A Methodology for Handling 

Standards Terminology for 

Sustainable Manufacturing 

Anantha Narayanan 

David Lechevalier 

KC Morris 

Sudarsan Rachuri 

Systems Integration Division 

Engineering Laboratory 

October 2013 

U.S. Department of Commerce 

Cameron F. Kerry, Acting Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 

NISTIR 7965 
1

Abstract

To develop the discipline of sustainable manufacturing, the language of 

discourse needs to be properly and clearly communicated, for both manufacturers 

and consumers. As a result, a range of information standards that define the needed

terminology is emerging. These standards span a wide range of technology, as well 

as geo-political areas, and serve multiple purposes. The task of understanding the 

challenges in achieving a sustainability objective is difficult when non-experts have 

to browse through several pages of document standards to find relevant 

information. Traditional document style standards and dictionary style definitions 

are limiting when it comes to getting a holistic picture of requirements imposed by 

the business objectives of sustainability. Our objective is to build a classification 

system for sustainable manufacturing terminology and a repository for knowledge 

dispersion to improve a reader’s comprehension of this information and support 

decision-making. In this paper, we present an informatics approach to synthesize 

and classify the terminology defined in standards by studying the structure and 

relationships within standardized lexicons. 
2

Table of Contents

1 Introduction ....................................................................................................................................3

1.1 Informatics Approach ........................................................................................................4

1.2 Related Work.........................................................................................................................5

1.2.1 Standards for Sustainable Manufacturing.............................................................6

1.2.2 Related work in taxonomies .......................................................................................6

1.2.3 Knowledge organization ..............................................................................................7

1.2.4 Information Visualization............................................................................................9

2 Knowledge Representation in NOVIS....................................................................................9

2.1 Classification Scheme.........................................................................................................9

2.2 OWL Ontology .................................................................................................................... 12

2.3 Populating the Ontology................................................................................................. 12

3 Using NOVIS.................................................................................................................................. 13

3.1 Visualization ....................................................................................................................... 14

3.2 Query Engine ...................................................................................................................... 16

4 Summary and Future Work.................................................................................................... 17

Disclaimer .......................................................................................................................................... 18

5 References..................................................................................................................................... 18
3

1 Introduction

Technology is evolving at a rapid rate today. As part of that evolution a network of 

information standards also evolves. Standards span a wide range of technology 

areas and serve multiple purposes. For instance, performance standards ensure 

that technology components perform as expected so that the components can be 

integrated into larger products and enterprises. Other standards define business 

processes to support interoperability between enterprises. Still more standards 

prescribe regulations that constrain how different materials may be used and limit 

the negative impacts of different processes and practices. This conglomerate of 

standards contributes towards an orderly yet rapid development of new technology. 

One of the functions of these standards is the containment of technology such that a 

sustainable system will evolve. The sustainability of the system can be measured by

the impact the system has on the overall environment in terms of the resources 

consumed and produced by the system. In the manufacturing area this evaluation is 

discussed as the study of sustainable manufacturing. To develop the discipline of 

sustainable manufacturing, a language of discourse needs to be properly and clearly 

communicated, for both manufacturers and consumers. Producers need to clearly 

understand and articulate their requirements and the services they provide, while 

consumers need to understand the meanings of that information to make informed 

choices. As such a lexicon of sustainable manufacturing is evolving and is often 

captured in standards documents. 

The rapidity of growth in this lexicon is further complicated through the 

globalization of commerce. The terms being used to discuss sustainable 

manufacturing are emerging in the form of research, policy, and regulations in 

locales all over the globe. Many of these are being formalized within standards that 

are also dispersed and disconnected. Concurrently, businesses are developing and 

producing products that will be marketed all over the globe. Businesses need to 

understand how their products relate to the concepts being promoted locally as 

sustainable manufacturing and at the same time have a global understanding of the 

sustainability of their practices. 

In this paper, we present an informatics approach to synthesize and classify the 

terminology defined in standards by studying the structure and relationships within 

those lexicons. Traditional document style standards and dictionary style 

definitions are limiting when it comes to getting a holistic picture of sustainability 

requirements. The task of understanding the challenges in achieving a sustainability 

objective is difficult when non-experts have to browse through several pages of 

document standards to find relevant information. Recently, many standards have 

been made available as electronic documents, searchable on the web. However, 

these searches are usually simple text searches, with no direct relevance to the 

semantic content of the standards. Our objective is to build a classification system of 

sustainable manufacturing terminology and a repository for knowledge dispersion 
4

to assist in sustainability decision making (for both manufacturers and consumers). 

Therefore, not only do we collect the relevant sustainable manufacturing 

terminology, we also define a means for classifying these terms and constructing

relationships between them. This structure will aid in improved understanding and 

decision- making ability.

1.1 Informatics Approach

Understanding the challenges of sustainable manufacturing can be a daunting task.

While there are a diverse range of standards that address various aspects of 

sustainable manufacturing, it is very challenging for non-experts to navigate 

through the standards documents to identify information, and devise a plan of 

action, to address their sustainability objectives. Our approach to this problem is to 

organize the standards information in such a way that different standards can be 

related while still maintaining the connectivity back to their authoritative sources. 

The terminology of the standards is synthesized and classified based on a multifaceted classification scheme. We develop an ontological schema to capture the 

main concepts in sustainable manufacturing, and constructed an ontology that 

classifies a wide range of sustainable manufacturing terminology. We take a 

standards-centric approach in capturing a set of standard terminology that address 

the most critical aspects of sustainable manufacturing. Finally, we produce a 

prototype implementation to demonstrate a visual interface built upon the 

underlying logical framework. The work is designed in such a way that 

contributions may be solicited from experts to expand the network of information 

by including a wide range of standards. We expect that this approach will be 

relevant to other areas of study beyond sustainable manufacturing.

Figure 1 depicts the architecture of our system, the NIST Ontological Visualization 

Interface for Standards (NOVIS)1. NOVIS uses the Web Ontology Language (OWL)

[1] for organizing terminology related to sustainable manufacturing. We develop a 

classification scheme for terminology related to sustainable manufacturing, by 

surveying the literature. We create an OWL representation of this classification 

scheme to be used by the NOVIS tool. Next, we extract terminology from a number 

of standards documents, and classify them according to the classification scheme. 

The collected terminology is represented as an OWL ontology, the technical details

of which are described in Section 2. A query and control engine, and a visual 

interface are designed to present the information contained in the OWL ontology in 

an intuitive way to the user. In our future development, it will be possible to use the 

query and control engine to interactively modify and update both the classification 

scheme and the OWL ontology. These aspects are described in detail in Section 3. 

We use existing software tools that process OWL to produce a prototype 

implementation of this approach. 

 

1 NOVIS is a software tool developed at the National Institute of Standards and Technology (NIST) to 

provide an interactive visual interface to the terminology used in a variety of standards related to 

sustainable manufacturing [21].
5

Figure 1: Architecture for NIST’s Ontological Visualization Interface for 

Standards (NOVIS)

The remainder of this section provides background on the multiple facets of the

work related to the project including various sustainability-related standards and 

work on knowledge representation and visualization. Section 2 goes into the details 

of the classification schema for sustainable manufacturing and the OWL 

implementation. Section 3 describes the query and visualization approach. 

1.2 Related Work

The existing work relevant to our project can be divided into two distinct categories:

 sustainable manufacturing standards

 knowledge representation technology

The standards related to sustainable manufacturing are the driving force in 

undertaking this project. Over the last several years numerous standards have 

emerged in this area. These standards often overlap and yet are different both in 

form and in where they apply. The text in these standards can often be difficult to 

interpret in different contexts. Different words might be used for the same 

meaning, while the same words might also be used but with somewhat different 

meanings in different contexts. Furthermore, the standards can apply across

different geo-political regions or in different industries. For an end-user,

understanding these differences and keeping track of the implications for their 

business can be overwhelming. 

Secondly, work on knowledge representation and the technology, which supports it, 

is also an evolving field. We propose a new way of defining terminology in 

standards that makes full use of the emerging technology in this area. This

approach enables intelligent navigation and advanced querying. Intelligent 

navigation uses colors and visual cues to navigate to related concepts as captured in

the ontology. We are experimenting with applying recent advances in the 

technology for graph visualization and visual dictionaries. Advanced querying 

enables queries to be constructed at varying levels of abstraction. For example we 

Classification 

Scheme

Classification 

Scheme in 

OWL

Standards 

Documents OWL 

Ontology

Conforms 

To

Derived From Visual 

Interface

Visualization

Query and 

Control 

Engine
6

can find all “related resources,” or narrow down the search to specific types of 

resources. In this case, we are applying ontology technology to the definition of a 

lexicon for sustainable manufacturing. 

1.2.1 Standards for Sustainable Manufacturing

Standards are defined as “the common and repeated use of rules, conditions, 

guidelines or characteristics for products or related processes and production 

methods, and related management systems practices.” [2] Standards play a crucial 

role in manufacturing, serving as best or recommended practices that 

manufacturers should follow to produce quality products. Regulations are used to 

specify mandatory requirements that must be met under specific laws, and 

implement general agency objectives. Many governments have enforced regulations 

that restrict the manufacture of products that pose a threat to our environment. For 

example, the Restriction of Hazardous Substances (RoHS) directive [3] is a 

European regulation that restricts the use of certain harmful chemical substances in 

electronic products, and is enforced in several countries. Regulations are often 

supported by voluntary standards that may serve as a basis for rule making or as 

general guidelines for compliance. For instance, IPC-1752 [4] is a material 

declaration standard that can be used to declare compliance with the RoHS 

regulation. Standards and regulations play an important role in sustainable 

manufacturing due to the complex nature of cross-domain concerns it involves. 

In recent times, we have seen an increasing number of standards and directives 

related to sustainability. Previous work at NIST focused on characterizing and 

cataloging a number of these standards [5]. This work highlighted the importance of 

taking an informatics approach to resolve the complexities and ambiguities in these 

standards. One of the important issues was the use of terminology in different and 

sometimes conflicting ways. Moreover, information about these standards is usually 

provided by disparate bodies and often does not consider wide ranging impacts and 

relations to other bodies of work. In a workshop on sustainable manufacturing 

conducted at NIST in 2009 [6], industry participants identified access to and 

understanding of information as the main hurdles in incorporating sustainable 

practices in their businesses. One of the requirements identified was the need for a 

consolidated repository of information that takes a holistic view of sustainability 

standards, and allows users to navigate through various related standards along 

clearly defined logical and physical relationships.

1.2.2 Related work in taxonomies

The classification scheme we have adopted in this paper relies heavily on certain 

existing taxonomies. The Manufacturing Processes Reference Guide [7] provides a 

taxonomy of manufacturing processes. This taxonomy is based on the Fabrication 

Process Taxonomy [8] and organizes processes in a tree taxonomy based on their 

impact on the materials that are processed. Figure 2 shows a portion of this 

taxonomy. Xue et al. [9] recommend using such a process classification system to 

understand the nature of the process and make environmentally friendly choices. 
7

Our work takes advantage of these existing taxonomies to provide a classification 

scheme for sustainable manufacturing.

Figure 2: Portion of manufacturing process taxonomy [7]

Snomed CT [10] is a taxonomy related to health care. It resulted from the merging of 

two terminologies and contains more than 311,000 concepts. This taxonomy is 

enriched by relationships that add a semantic meaning to the hierarchical

organization. This taxonomy provides standardized definitions for medical 

terminology. The electronic health record system is based on this classification. We 

propose a similar approach to semantically enrich existing taxonomies related to 

manufacturing, to provide a classification scheme for sustainable manufacturing

standards.

1.2.3 Knowledge organization

The knowledge organization technology that has influenced our work is, first, that of 

ontologies to represent information and, secondly, work in visual dictionaries for 

presenting information. 

Ontologies are used in several domains, like biology, health care, and even natural 

language processing, to create classification schemes for these domains. Perhaps 

the first formal classification schemes were taxonomies. Taxonomy was originally 

the discipline of classification of living organisms in bacteriology, botany and 

zoology. However, its use has now spread to other sciences and the term taxonomy

is used to describe systems or hierarchical classification methods for inventorying 

objects, concepts, information of a given area to:

• determine the behavior to adopt towards a given object

• predict the behavior of an object

• understand a phenomenon to be able to then act

Where taxonomy is a tree-based classification scheme, other schemes may form 

networks of classification. Trees are useful for decomposing a single domain into 

mutually exclusive classes; however, in a multidisciplinary world, such as that 
8

covered by sustainable manufacturing, classification networks are useful for 

crossing domains. Our work proposes a set of taxonomies in different disciplines, 

such as standards documentation, and relates these through classifications which 

span the taxonomies. This organization is suitable for our goal of supporting 

comprehension and retrieval of information concerning sustainable manufacturing. 

In contrast the traditional form of representing terminology in standards is 

dictionary based. In a dictionary, it is difficult to trace relationships between terms 

and other concepts. For instance, a dictionary definition does not explicitly capture 

relationships between a term and other terms, other standards, or sustainable 

manufacturing concepts. By building a classification scheme, we explicitly establish 

these relationships not only as links to other concepts but also as relationships with 

a semantic meaning. Capturing semantic relationships presents great possibilities, 

such as context-driven presentation of information and advanced querying 

capabilities. Such a semantic network can be implemented using an ontology.

To facilitate search, an ontology is used as a structuring device for an information 

repository (e.g., documents, web pages, names of experts); this supports the 

organization and classification of repositories of information at a higher level of 

abstraction than is commonly used today [11]. An ontology is a semantic network 

that allows a user to follow a stream of thought through querying and navigation, as 

opposed to a loosely related list of search engine results [12].

Perhaps the most widely adopted language for representing ontologies today is 

OWL. OWL was developed for connecting disparate information across the Internet, 

i.e., the World Wide Web. It is suitable for connecting information contained in a 

wide variety of formats, in dispersed locations, and organized as loose and flexible 

networks. OWL ontologies are based on a set of axioms, which define classes and 

relationships in order to contain the data. OWL is designed for being semantically 

interpretable by computer applications, and not primarily for human readability.

A recent effort from the World Wide Web Consortium (W3C) into what is known as 

Simple Knowledge Organization System (SKOS) [13] is a more specific set of 

standards for representing structured controlled vocabularies. SKOS is built upon 

the Resource Description Framework (RDF) [23]. It may be used on its own, or in 

combination with more-formal languages such as OWL. There are many software 

tools that support the OWL format and more are emerging that are specialized for 

SKOS. Protégé [14] is an open-source application for creating, visualizing, and 

manipulating OWL ontologies. Additional plugins may be developed for Protégé for 

specific tasks. In particular, a SKOS Editor developed by the University of 

Manchester makes it possible to create and manage SKOS vocabularies [24]. 

SPARQL [15] is a query language used to retrieve and manipulate data in RDF and 

OWL. When using SKOS with OWL, we may use SPARQL to execute queries on the 

ontology. Based on these queries it is possible to extract specific information that is 

contained in the ontology.
9

1.2.4 Information Visualization

Dynamic visualization tools are useful to help users quickly understand and 

evaluate the data. For navigating, information visualization technology [16] has 

been developed to interactively show concepts. In this context, information 

visualization technology is a means by which concepts or data are graphically 

represented to facilitate comprehension. The technology is becoming increasingly

popular in our net-centric world. One example of such technology is the graphical 

dictionary Visuwords (http://www.visuwords.com/) based on Wordnet, a large 

lexical database of English [17]. Another example is DebateGraph [18], which is a 

cloud-based service that offers individuals and communities a way to learn about 

and deliberate and decide on complex issues. According to Nicholas H. Lurie & 

Charlotte H. Mason in 2007 [19], there was no doubt that visual representations will 

become more prevalent for decision making. For instance, business intelligence is a 

set of theories, methodologies, processes, architectures, and technologies that 

transform raw data into meaningful and useful information often displayed as 

graphs.

2 Knowledge Representation in NOVIS

In this section we describe the NOVIS classification scheme, how we turn that into 

an OWL ontology, and finally how that ontology is populated with the contents of 

various standards.

2.1 Classification Scheme

Our approach to building the classification scheme is to define taxonomies for each 

of the various categories of concepts related to sustainable manufacturing, and to 

capture relationships of the concepts between these taxonomies in a schema or 

package. The terminology used in sustainable manufacturing is classified 

accordingly. This separation allows for concurrent and distributed development of 

the system. The taxonomies in the classification scheme are shown in Figure 4.

They are arranged in a hierarchy. We use a combination of abstract concepts and

concrete concepts to facilitate the task of capturing relationships between the 

different facets of the classification scheme. The Core package is unique in that it

defines abstract constructs for classification, which are used by and refined in the 

other packages. For example, it defines the abstract concept Classifier, which allows 

the concepts in the taxonomies to be specialized in similar ways between the other 

packages.
10

The Standards package defines concepts such as Standard and Term. Using this 

scheme, we can define low level relationships between the concrete concepts 

Standard and Term, or define a more abstract relationship between the concept 

Term and any other concept which is a type of abstract concept called Classifier. As 

we will see later, this will allow us to capture a complex network of information and 

construct queries to find information easily. Besides the abstract Core package, we

have identified four main packages: Manufacturing, Measurement, Standards, 

Figure 4: Overview of Packages in the Schema

Figure 2 Overview of Packages in the Schema

Figure 3: Overview of Standards Package
11

and Resources packages. The Manufacturing package covers concepts related to 

manufacturing including process types, product categories, and product life cycle. 

The Measurement package focuses on the evaluation of physical quantities related 

to the sustainable manufacturing. The Standards package contains concepts such as 

Standard and Term, which classify standards and terminology in sustainable 

manufacturing. The Resources package covers the material used in the sustainable 

manufacturing. These may be further divided into sub-packages. This arrangement 

allows the individual packages to be developed by domain experts, and integrates 

them at a higher level. In the rest of this section, we will focus on the Standards

package, since our initial goal is to extract, organize, and store terminology 

contained in standards documents. Other packages will be organized in a similar 

fashion.

The Standards package is shown in Figure 3. In this package, we first define the 

abstract concept called StandardsElement as a subtype of the abstract concept 

Element from the Core package. We define the concrete concepts SDO, Standard, 

Term etc. as subtypes of the abstract concept StandardsElement. The concept SDO

represents standards development organizations. The concept Standard is used to 

represent individual standards, and the concept Term is used to represent 

terminology from standards (terms may be single words or phrases). We have also 

defined the following relationships between these concepts. A Standard can contain 

several Terms. This is captured by the relationship termInStandard between the two 

concepts. A Term can be related to other Terms using relationships such as 

referencesTerm or similarTerm, and also with other elements of our schema using 

relationships such as appliesTo.

Figure 5: System boundary term and its relationships
12

For instance, consider the term system boundary from the Standard 

PAS2050:2011 [20]. We can symbolize this connection between the term and the 

standard with the composition relationship named termInStandard. Furthermore, 

in its definition, the term contains several other terms like unit process. We can 

symbolize this connection with the relationship named referencesTerm. Figure 5

illustrates this example. As you can see, system boundary is also referenced by the 

term life cycle GHG emissions. Moreover, since the schema defines the appliesTo

relationship between Term and the abstract class Element, you can relate any Term

to a specialized Element from another package. As mentioned above, we have 

surveyed several standards developed by different SDOs (Standards Development

Organizations) to extract the relationships and classes defined in the Standards

package.

2.2 OWL Ontology

The taxonomies described in the previous section are represented as an OWL

ontology for use in the NOVIS application. The following OWL mechanisms are used 

to represent the classification scheme as an OWL ontology: 

 Classes correspond to the classes identified in the taxonomies. The classes 

are all rooted in the “Thing” class which is fundamental to OWL. We do not 

make a distinction between abstract and concrete concepts in OWL, due to 

technical differences in the way OWL concepts are treated2.

 Data properties are used to capture the attributes or properties of the 

concepts.

 Object properties are used to capture relationships between concepts.

Once we have created the OWL ontology representing the classification scheme, we 

can populate this ontology with terminology extracted from standards documents, 

by creating OWL individuals of the appropriate classes.

2.3 Populating the Ontology

Once the schema has been encoded in OWL, we add terminology from standards to 

populate the ontology. We do this by manually extracting terms from a standard, 

and classifying the terms based on the schema. Initially, this is a tedious and timeconsuming task that requires domain expertise and a good understanding of the 

classification scheme. However, we are developing tools and techniques to simplify 

and automate this task. For instance, we have developed a plug-in for the Protégé 

tool that reads terms and definitions from a Microsoft Excel file and imports them 

into the OWL ontology. 

Terms extracted from standards documents are encoded as OWL individuals of the 

class Term. The standard’s definition of the term is entered as the value of the 

definition data property for the corresponding OWL individual. We investigate the 

 

2 In OWL, the relation between classes and the instances they represent is purely logical. An 

‘instance’ of a class in OWL is also considered an instance of all of the super-classes of that class.
13

definition to find other related terms and standards, and establish these relations in 

the ontology. 

As an example, consider the PAS2050 standard. We create an OWL individual of the 

class Standard for this standard, and set its name and description properties 

accordingly. We then manually add the terms from PAS2050 to our ontology. For

example, PAS2050 defines the term “anticipated life cycle greenhouse gas 

emissions”. We create an OWL individual in our ontology to represent this term. 

We transcribe the name and definition as data properties of this individual. In OWL 

the term is related to other terms through object properties. For instance, 

“anticipated life cycle greenhouse gas emissions” is related to the PAS2050 standard 

through the object property termInStandard. The value of this property is set to be 

the OWL individual named “PAS2050”. Moreover, the term contains other terms in 

its definition. In the case of this definition, some terms are explicitly called out 

through the use of the standard’s own numbering system but that is not always the 

case. These relationships are also captured through object properties but are 

distinguished by the use of different types of object properties. In this case the 

object property named referencesTerm is used to relate the terms. Similarly, the 

“anticipated life cycle greenhouse gas emissions” term is related to the other terms

by using this object property. In this way, the schema allows us to capture various 

terms as well as relationships between terms.

Capturing the terminology as a network of information in this manner allows one to 

develop advanced user interfaces for the information. The network can be 

displayed as a visual graph to aid comprehension of the information. Also, advanced 

queries can be written based on the relationships represented, thereby facilitating 

the use of the standard. The following section describes the NOVIS tools that 

demonstrate this approach. 

3 Using NOVIS

Based on the ontology described in the previous section, we have developed two 

ways to access the information: an interactive visual interface for viewing the 

network of terminology and a query interface for locating information. As a large 

graph or network of information, we make use of established methods of viewing 

and navigating graph networks to visualize the ontology. We have also developed a 

query interface for generating advanced queries by creating a set of predicates to 

describe the concepts and relations defined in the ontology. Here we leverage 

SPARQL [15], a query language applicable to OWL, though the user does not require 

any knowledge of SPARQL to use the advanced query interface. Note that the NOVIS 

interface is designed to operate independently from a given ontology. In other 

words, any OWL ontology meeting certain requirements can be loaded into the tool 

as is explained in [21]. This separates the interface concerns from those of the 

particular ontology being used and allows the ontology and interface to evolve 
14

separately. This feature is important as new discoveries are constantly being made 

in the area of sustainable manufacturing, causing the standards and other 

information in this area to grow and evolve rapidly. 

3.1 Visualization

Visualization in NOVIS is designed to be interactive and intuitive and is guided by 

the classes and relationships that compose the ontology. We have used the open 

source API called Prefuse [22] to build our ontology visualization tool. This toolkit 

allows us to quickly create customized representations of the different types of 

classes and relationships in the ontology and present them in a graphical form. The 

classes and individuals from the ontology are represented as nodes in the graph. To 

distinguish them, the classes are represented by rectangles, and individuals are 

represented by rounded rectangles. To distinguish the classes themselves each is 

represented as a different color. Arrows symbolize the relationships in the 

ontology, which have the underlying representation of object properties between 

the individuals. The direction of the arrow corresponds to the ownership of the 

relatio nship. Data properties are not included in the graphical representation but 

are displayed in text format as is shown in Figure 6. Each element in the interface, 

whether graphical or text-based, is interactive. 

Figure 6: NOVIS Visualization Tool

1 

2 

4 

3 
15

The interface as shown in Figure 6 consists of the four parts described below:

1. Graphical View: The box on the left shows a graphical view of the selected 

term, and its immediate neighborhood of related terms, standards and other 

elements. This is a clickable interactive interface. The terms are presented as 

a graph where the nodes represent individual terms, standards and other 

concepts, and edges represent relationships between these concepts. Colors 

and shapes are used to aid in understanding the types of these concepts. This 

visual representation provides an intuitive overview of the term and its 

immediate context. Clicking on a node in this view redraws the graph by 

opening the clicked node and displaying its immediate neighborhood of 

related nodes. This allows the user to navigate through the large and dense 

underlying network of information by viewing a small portion of it at a time, 

and traversing relations that are of interest.

2. Descriptive Information: The information panel on the right shows 

descriptive information about the selected item. For example, when a term is 

selected in the graph on the left, the information panel gives information 

about that term, such as its definition and source. The definition text itself is 

hyperlinked, so that clicking on certain phrases in the definition will lead the 

user to other terms that will help him/her better understand the 

terminology. 

3. Quick Search: The quick search box on the right of the tool window provides 

a simple string search, which matches partial strings in the ontology to give a 

quick list of results. Note this is much more basic than the advanced search 

that is described in the following section.

4. Navigation Panel: The navigation panel at the bottom assists in navigating 

the information network. Forward and back buttons are provided for simple 

navigation through terms viewed so far. More advanced options include the 

ability to group the terms based on certain properties, such as a term’s 

relationship to specific standards, to assist in more directed navigation of the 

information network.

The navigation panel and other aspects of the interface are described in more detail 

in [21]. One feature of the navigation panel that is especially useful for 

comprehension is the ability to filter based on values for different types of 

relationships. This feature is accessible through the “Group By” selection in the 

navigation panel. With Group By a user choses the type of relationship of interest 

and then selects a value for that relationship. For example to view only those terms 

within a particular standard, one would chose the termInStandard relationship and 

provide the value for the standard of interest, perhaps PAS2050 from the example 

above. The interface then highlights anything in the graph that is contained in that 

standard.
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3.2 Query Engine

The tool supports an advanced query interface, which allows the user to specify 

search predicates based on the schema. The SPARQL language is used for querying 

the ontology. We have designed this interface to allow users without any knowledge 

of OWL or SPARQL to define the predicates, by directly referring to the concepts and 

relationships defined in the schema. Figure 7 shows the query interface window. 

Each row at the top of the window is a predicate. The user can add or remove rows 

to customize their query. The Qualified name column identifies the concept or 

relation in the schema that the user is interested in, and plays a crucial role in 

forming the query. To facilitate non-experts in generating specific queries, the dropdown lists in this column provide ready access to the concepts and relationships 

defined in the schema. For example, the user can search for a specific type of 

element, such as Term, and constrain the search by specifying additional constraints 

on the properties of the matched individuals. The middle box in the figure is the 

OWL query in SPARQL which is automatically generated from the predicates. The 

list at the bottom of the window contains the search results. Clicking on an item in 

this result list shows that item in the visualization window, along with its 

description and immediate relationships to other elements.

The query in Figure 7 is designed in the following way. The user wishes to find 

terms that are related to the metal fastening operations. The user generates a query 

by creating the following predicates: 1) match a term by finding individuals of type 

Figure 7: Advanced Query Interface
17

Term; 2) match only those terms that apply to the ‘fastening’ process using the 

relation appliesToProcess; and 3) match only those terms that also have the word 

‘metal’ in their definition. Executing this query presents the relevant terms, and 

leads the user to other relevant standards and sustainability concepts by following 

the relationships in the visualization. This kind of information discovery is far more 

difficult using traditional document-based terminology repositories. 

Note that to use the advanced query interface, the user must be familiar with the 

schema, even though no detailed knowledge of OWL or SPARQL is required. We 

believe that a well-constructed ontology will be easy to understand and use with 

very limited training. To assist the user in making the best use of the query 

interface, the tool provides easy access to the schema elements as drop-down lists, 

and also a tool tip describing the meaning of the chosen element.

4 Summary and Future Work 

Sustainable manufacturing is a rapidly developing area. An understanding of the 

global necessity for sustainability has led to rapid development of new standards to 

aid sustainable manufacturing. An important part of correctly understanding and 

interpreting these standards lies in the correct interpretation of the terminology in 

standards. In this paper, we have shown an informatics approach to communicating 

standards terminology by categorizing them using a well-defined schema, and 

providing an intuitive visual navigation mechanism to learn about them. An 

advanced querying interface allows users to find terminology- and standardsrelated information easily. This framework allows manufacturers to find crucial 

standards-related information, without having to learn the tedious details of how 

standards documents must be read and interpreted. 

In our future work, we will enhance the schema to cover the wide range of areas 

that impact sustainable manufacturing. We will also survey more standards 

documents and to create an OWL repository of standards that can be navigated 

visually and queried upon using our framework. 

While the main goal of this work is to improve user comprehension of standards 

related to sustainable manufacturing, we also believe that this framework can aid 

the development of the standards themselves. One of the most painstaking tasks in 

the development of standards is the collection and consolidation of terminology to 

be used in the standard. The development and implementation of standards can be 

greatly accelerated if it is easier to manage the terminology in standards. Our future 

work will investigate the application of our framework for standards development. 

Our framework can facilitate the reuse of terminology from other standards, and in 

keeping track of changes to definitions of terms in related standards. The 

maintenance of standards can also be improved by applying our framework to 

record, store and maintain standards information.
18

Disclaimer

Certain commercial software products or services may be identified in this paper. 

These products or services were used only for demonstration purposes. This use 

does not imply approval or endorsement by NIST, nor does it imply that these 

products are necessarily the best for the purpose.

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