4 Operational View

The operational view consists of the pilot system, menu of options, and development
phases. The initial application context will focus on determining the remaining service-
life of a coating, performing coating failure analysis, and then identifying the optimal
corrective response. The optimal response will be based on life cycle costing and could
include doing nothing, providing in-house or an out-sourced repair, or an in-house or
out-sourced replacement action. The operational view will focus on addressing this
application requirement.

4.1 Pilot System

Figure 3 shows a pilot system layout between the major components, described below.

Pilot —

Internet F

Web Onto- Info
Server Frame = DBMS DMS Retriev
Agents| Legacy)
& CBT ES
Collaborative 7
Environment CLIPS

Servers
Processes

bases

Oo Applications

Translation
Procedures

Dss Conver ewvoaneny
IKIF-like
ES to DSS, Key
ES CLIPS, =e
etc. oe
Figure 3

1. The Web server provides users with access to the system through the application
interfaces (e.g., decision support system (DSS), Web Expert System (Web ES), and
computer-based training (CBT)). The web server will interact with all of the other
servers. It is also considered the front door where all users would enter a web-
based knowledge center, therefore it must all fit within an intelligible user interface.

2. WinFrame is a server product (as mentioned in section 2.3), which allows
WindowsTM applications to run through a web browser. This technology will provide
an interim capability until existing applications are reengineered to operate
completely on the web.

3. Ontolingua provides the knowledge repository, and uses first order predicate logic
and frames for representing knowledge. Currently, work is underway to test the

16


Data segment Example data elements

Product description Product name, product identification,
generic type, system component
Intended use Common application, substrate type,

exposure environment, compatible
undercoat and topcoat

Physical properties Volume solids, solids by weight, mixed
density, test methods
Mixing and application Minimum and maximum dry film

application thickness, theoretical
coverage per volume, dew point,
induction time, pot life

Key performance parameters Corrosion resistance, weathering,
abrasion resistance, test methods
Safety NFPA health hazard, flammability

Manufacturer supplemental information Manufacturer comments

Table B-1: Example of product data segments and data elements.

The draft document is being proposed as a SSPC Guide and would be used by coating
manufactures, specifiers, and users (facility owners) for communicating coating product
data. Figure B-3 is a diagram of the use of coating product data using the Ww’. Product
data sheets are now used to communicate this information. However, variations exist
among manufacturers when describing product data content, such as terms used and
the type of data reported. As more companies use computerized systems such as the
W* to disseminate information, standards such as the proposed coating product data
guide will provide the mechanism for improved understanding (through consistent
terminology and data elements). This will enable integration of product data among
diverse computerized systems, such as company and facility owner project databases.
Another benefit of the guide will be an increased understanding of coating material
performance and data quality through the reporting of data elements such as those
described in the “Key Performance Parameters” data segment. These parameters will
be substantiated through the identification of test methods used to develop parameter
values.

37



Appendix B: CIKS Coating Partnership for Highway Structures

B.1 Overview of the Partnership

Figure B-1 shows a diagram of a proposed partnership to develop a unified decision-
support system for highway bridge coatings using the CIKS framework. Each of the
organizations identified in the diagram is currently active in developing information,
standards, or computerized systems that address the needs of highway engineers and
contractors. With regard to computerization of information, however, there is no unified
effort to leverage each organizations work. Recognizing that each organization has its
own area of special interest and objectives, it is still possible that this can be achieved
and provide consistent methods to enhance decision-making for highway personnel.

One of the main objectives of the partnership would be to adopt standard terms, data
formats, and information models so that individual computerized systems or modules
would complement each other. Terms and data would be consistent among modules.
An example is the coating product data format being proposed as an SSPC guide. This
effort should be extended to include additional parameters for representing coating
performance properties.

Information models could be useful in the development of modules, initially through the
partnership, and ultimately by the entire coating industry (e.g., SSPC standards, and
guides). An information model would contain references to standard terms and formats,
criteria for data quality, and guidance to developers. It would provide a systematic
approach for decision-making. For example, it would outline essential steps and
information requirements for a coating management system for highway bridges. Steps
could include identification of coating failure, diagnosis and remedial action, coating
system selection, maintenance consideration and procedures, and assistance in
developing contract specifications.

An engineer would view the system as a single system, comprised of modules (e.g.,
coating selection, failure analysis, and maintenance) that provide information, guidance,
and references. Standards of practice, such as surface preparation, inspection
procedures, data formats, and terms would be referenced and obtained from SSPC.
Accepted field guides to practice could also be used. Interoperability among the
modules could be by user request or automatic. For example, to substantiate a system
recommendation involving low volatile organic chemical coatings, the "system" may
reference work performed by FHWA that is contained in a database maintained at the
FHWA site. Or the system could draw upon an archive (photographs, video) of coating
failures that provides the user with detailed information on causes, exposure
environments, and remedial actions to be taken based on individual case-specific
needs. Emphasis would be placed on information in digital formats and the Internet
would be the dominant vehicle for access to the system. Considerations would be
made for information of a commercial and proprietary nature.

33



result is a significant reduction in development time. Additional benefits in using object-
oriented development tools include:

ability to reuse data, knowledge, and procedures;

relationships can be established among data and knowledge (inheritance);
efficient graphical user interfaces are included in the tool;

improved interfaces to external knowledge and programming modules.

B.7 COEX-I: An Object-Oriented Decision-Support System

One benefit of implementing decision-support systems is their ability to provide a
systematic approach to problem-solving and knowledge dissemination. Incorporating
the knowledge of experts in coating materials and practices can improve levels of
decision-making. For example, experts residing in a central location can extend or
replicate their knowledge to field staff who need to evaluate the condition of coated
structures, and perform repair and maintenance duties. Guidance for these individuals
is typically found in manuals, guides, and standards. Computerizing the knowledge to
include photographs, sound and video, and guidance on the use of the guide
significantly enhances knowledge understanding, resulting in cost savings, and
improved facility performance.

The COEX-| expert system was developed by a team of coating experts who had
previously written the Military Handbook, “Handbook for Paints and Protective Coatings
for Facilities” [DOD95]. The group includes representatives from Department of
Defense facilities who are involved in maintaining coated facilities. The group decided
to develop a prototype decision-support system to assist military staff in analyzing
coating failures that occur on stationary military structures such as water towers,
buildings, and bridges. Section 11 of the guide covers the Analysis of Paint Failures
and includes a decision tree that is designed to assist the user of the handbook. From
this decision tree, rules (logic) were computerized in the form of questions-and-answers.
The rules provide a hierarchical structure to the knowledge and guide the user in
problem solving, from the identification of visual observations found on the structure to
recommendations given by the system. Recommendations include the identification of
the coating failure, its cause, and guidance on remedial action(s). An additional
capability was added to allow the user to specify criteria for coating system selection
where total replacement is necessary for structural steel that shows blistering to the
substrate. Figure B-7 shows a diagram of the COEX-| system.

42


The focus of CIKS will initially be on document-based knowledge (e.g., handbooks,
practice guides, codes, etc.) instead of human domain experts for our models.
Therefore, natural-language processing (NLP) technology is being evaluated for use in
automated knowledge acquisition of machine-readable documents. Current academic
research [HBCAD91, HS95] indicates the potential of this technology. Advances in this
area will increase the potential for building knowledge bases and overcoming one of the
major bottlenecks in building smart systems. The main goals are to, 1) create a lexical
knowledge base from a machine-readable dictionary; 2) create a Knowledge
Interchange Format (see section 3.3.3) representation from a text document using a
lexical knowledge base; and 3) create a machine-readable dictionary from text
documents. If this were successful, knowledge that is currently locked in machine-
readable documents could be incorporated into operational systems.

3.2 Data Management

The data management part is divided into standards, models, and tools. Data
management is an integral part of any knowledge base since knowledge-like behavior
requires access to large amounts of relatively static data. In fact, some databases
could store all of the knowledge management sub-elements as drawn in figure 3 in
section 4.1 and as mentioned in the introduction to the Management View section.

ISO 10303 Standard for the Exchange of Product Data (STEP) and ISO 11179
Specification and Standardization of Data Elements will be used in the framework where
appropriate. In addition, the ASTM Committee E-49 on "Computerization of Material
and Chemical Property Data" established fundamental principles on data elements and
standards, and is being used as a basis for developing product data formats for
coatings and concrete materials. The efforts of other organizations will also be utilized.

Standard data elements within data models are similar to terminology in documents,
and are considered fundamental to data sharing for non-geometry (e.g., non-CAD) type
data. The same representational concerns that exist for terms and concepts apply to
data elements. Therefore, standard data elements should be developed and refined by
defining them based on an analysis of an area's terminology where possible (see the
next section). One of the first steps in building the CIKS framework will be identifying
and agreeing upon standard data elements for data sharing or exchange. One example
of the scale of the effort is the ongoing Department of Defense (DoD) effort [W°-2] in
developing its enterprise-wide models and registering standard data elements, which
total between ten to fifteen thousand. Other examples of the establishment of standard
data elements and data formats for construction materials involve the Society for
Protective Coatings, Committee C.4.10 which is developing a standard for coating-
product data format and the American Concrete Institute, Committee 126 which is
developing material property formats for the constituents of concrete [Kaet97].

10


technology. This would be the beginning of a digital library of relevant engineering
knowledge or know-how documents concerning condition assessment, failure
analysis, and economic decisions.

5. The next step requires reviewing metadata (data about data) standards and deciding
which standards will be adapted.

6. After that step, identifying a few standard data elements is similar to agreeing on
terminology, but within the database context.

7. Finally, assuming that how-to or knowledge documents are created by the
organization, a collaborative environment should be defined and implemented, which
could be as simple as using email, some workflow package, or selecting a web-
based collaborative document editing environment.

Appendix B includes additional details and provides another example of the
implementation details of CIKS.

20


Database design Database Database

& data sources implementation access

www

cHents

Manufacturer
product data

Coating
manafacturer
site database

Facility owner
database or decision
support system

pore }
‘query \

Knowledge Center
or facility owner
site database

Proposed SSPC
Product Data
Guide

Figure B-3: Diagram showing the generation and
use of coating product data.

B.4 Current NIST Effort to Establish the CIKS Test Bed

NIST is seeking to improve the delivery of its research results. The CIKS test bed will
be a useful tool in providing construction material knowledge. The AMRL Paint
Proficiency Sample Program is an example of the use of the test bed to provide access
to technical data. Technical databases and decision-support systems are methods that
have received the greatest attention thus far. Material property databases for cement
and coatings can now be accessed through the w.

Two decision-support systems have been developed by NIST during the past several
years. The first of the two systems is the Highway Concrete Expert System
(HWYCON). This system is designed to assist highway engineers in the diagnosis,
selection, and repair and rehabilitation of highway concrete structures. It includes
knowledge related to concrete pavements, bridges, and support structures. Several
universities as part of their material science and civil engineering curriculum are also
using the system. The Transportation Research Board sells HWYCON. The
computerized system requires the Microsoft operating system, Windows version 3, or
Windows '95. The distribution package contains a set of floppy diskettes and a report
describing the design, installation, and operation of the system [KCS94, KCKS93]. The
second system called the Coating Expert Advisory System-| (COEX-l) [BFRL96]
contains coating material knowledge and is designed to assist in the analysis of coating
failures and selection of coating systems for stationary military structures. An overview
of the COEX-| is described later.

38


1 Introduction

The construction community is represented by a diverse set of private and public
organizations and disciplines. The industry lacks consistency in the way it represents,
communicates, and interprets information about its products, materials, and systems.
There is a need to establish a framework that will improve the service-life and durability
of structures and components, and reduce costs associated with construction,
operations, and maintenance activities. Industry-scale improvements can result by the
adoption and implementation of a framework that includes:

consistent terms for use by construction industry knowledge users;
criteria and standards for data quality and formats;

a standard format for knowledge interchange;

improved methods for seeking and interpreting knowledge;

an intelligent interface for users.

This report outlines a proposed framework to address the needs of the construction
industry product, materials, and systems knowledge users involved in operation and
maintenance activities, such as condition assessment, material failure analysis, and
material selection. The report is published in both printed and digital form. Comments
on implementation strategies and opportunities can be provided to the authors on-line
through the World Wide Web (W) site for six months after publication. The framework
can be reviewed and comments submitted to the Computer Integrated Knowledge
System (CIKS) developers by pointing a W° browser to:

www.ciks.nist.gov/framework.html

Refinements to the CIKS framework will be ongoing in the W° document and updated
periodically in a NIST report.

CIKS is intended to be used for constructed facilities, such as bridges, private and
public buildings, etc. Initially, the framework addresses the industrial coating material
area. However, the activities and methodologies described will apply to other
construction materials, such as cementitious materials, steel, aluminum, composites,
and roofing.

Several goals have been established for the implementation of CIKS. These goals
provide a context for its development, a long-term vision, and near-term usefulness. It
is envisioned that refinements to the framework will occur as user needs change and
are better understood by the CIKS developers and partners, and as enabling
information technologies emerge over the 5-year development life of CIKS. CIKS will
show concept and provide value within a two-year time frame, yet maintain a five-year
development life. This will be achieved through the establishment of NIST/construction
industry partnerships and the establishment of a test bed whereby partners can test
production systems and data for interoperability, and obtain developmental and
implementation solutions for incorporation into industry-developed systems.



Davis, Shrobe, and Szolovits [DSS93] argue that knowledge representation can be
understood by the roles it plays. The first role, as a surrogate, is defined as a substitute
for the reasoner. Complete models of the natural world cannot be built, something is
always ignored, and that something may cause undesired consequences.

The second role, as a set of ontological commitments, could be translated to what is
relevant from a domain expert perspective. For example, a materials engineer would
model and acknowledge different objects then a material scientist or researcher would.
What is relevant, is affected by the selected representation, including what, where, and
how you put knowledge into that representation.

The third role, as a fragmentary theory of intelligent reasoning, is based upon the
premise that any model is only a partial representation (nothing is complete) and one
must understand which characteristics of intelligent reasoning are acceptable and
recommended. Davis et al. [DSS93] identified logic, psychology, biology, statistics, and
economics as the fields that provided insight about intelligent reasoning. Typical
examples of respective approaches include first order predicate logic, frames and
production rules, neural networks, uncertainty and fuzzy logic, and rational agents from
utility theory. Inferences, which are appropriate, based on the available information are
considered acceptable. However, when there are too many acceptable inferences, only
those recommended would be the intelligent ones. The authors state, “Representation
and reasoning are inextricable and usefully intertwined: A knowledge representation is a
theory of intelligent reasoning.” They also mention that logic is silent on recommended
inferences, motivated by the desire to work on all problems in the same way.
“Preventing the representation from selecting inferences and, hence, requiring the user
to do so offers the opportunity for this information to be represented explicitly rather
than embedded implicitly in the machinery of the representation.” However, the
opposing view is that the user is responsible for recommending a set of inferences
explicitly in the logical language, which places a burden on the user.

The fourth role discusses the trade-off between computational efficiency versus
representational richness, which is irrelevant from a representational perspective where
the goal is representation over efficient computation.

Finally, the fifth role, as a medium of human communication, refers to the distinction
between machine interoperability and human communication. Does this representation
make it easy for machines to interoperate, but difficult for human discussion?
Alternatively, is the opposite true, making it easy to communicate between humans, but
difficult for machines?

In a soon to be published book entitled Intelligent Systems for Engineering: A
Knowledge-Based Approach [Srir95], model-based representation was discussed in the
chapter on knowledge representation. “The models cover what components make up
the system, what is the function of the system, how the components are connected, the
expected behavior of the individual components...”[Srir95]. This model-based

31



feasibility of using the DARPA Knowledge Sharing infrastructure, prior to going full-
scale. Assuming it is feasible, we will consider the effort to build the translation
procedures to go from Ontolingua into other representations (e.g., Web ES, DSS,
and SQL) for operations. The knowledge base shown in figure 3 is the repository for
the initial coatings knowledge.

4. DBMS is a web-enabled data base management system that could have many
databases within the DATA item shown.

5. Docs is the collection of machine readable documents which will be resident either in
the operating system file system, inside a database, or inside a document
management system.

6. DMS is a web-enabled document management system. It is possible to use either a
DBMS or the operating system file system to store machine-readable documents.

7. The Information (or text) Retrieval server would need to be considered based on
what kind of document management system is implemented for the scenarios of the
documents being in a file system, a DBMS or a DMS.

4.2 Menu of Options

Table 2 below provides options within the framework, and identifies the technology that
can be used in each row in the table. This table is not meant to be complete, but it
provides the general breadth of areas that need to be considered. The conceptual
representation will be discussed in the appendix, where parts of it are shown in the
context of the implementation. The data column is shown separately, although all of the
knowledge management pieces could be considered data and stored within a capable
database. As mentioned earlier, knowledge management is distinguished from data, to
focus on the knowledge management aspects of terms, documents, and knowledge
exchange.

The first row, primary real-world sources, identifies the source for developing the
particular column (data, terms and concepts, documents, knowledge). Within the
Knowledge Management area, it is easier to use documents as the initial primary
source than to consult domain experts. The second row identifies example standards,
communities and projects that will be considered as the CIKS framework evolves. The
third row (addressed in the appendix) presents examples of conceptual representation
alternatives, which though not inclusive, provides a set of choices. The fourth row,
exchange, or sharing option identifies known choices for exchanging or sharing data,
documents, and knowledge. As mentioned earlier, the exchange structures only
provide a syntactical mechanism, without the semantics. The semantics will be built
using a KIF or KIF-like structure, first by modeling the terms and concepts, then
evolving to the documents and data, and finally to the knowledge or know-how
representation. The fifth row identifies the tools that would be used during the
development of the data, terms and concepts, and documents and knowledge. Finally,
the last row refers to the commercial environment for operational purposes. For
example, in the data column a commercial database product would be selected which
would influence the choices of available development tools.

17



[HBCAD91]

{Hend93]

[Hovy97]

[HS95]

[IH83]

[Kaet97]

[KCKS93]

[KCS94]

[Kipf92]

[KSL194]

[Lede91]
[MLF95]

[NNGE92]

[NR96]

[Nwan96]
[PB92]

Press, Oxford, England, 1990.

J.E. Hodges, L.M. Boggess, J.L. Cordova, R. Agarwal, and R. Davis,
The Automated Building and Updating of a Knowledge Base Through
the Analysis of Natural Language Text, Technical Report MSU-
910918, Department of Computer Science, Mississippi State
University, September 1991.

P. Henderson, Object-Oriented Specification and Design with C++,
McGraw-Hill Book Company, New York, NY, 1993.

E. Hovy, A Standard for Large Ontologies, Presented at NSF
Workshop on R&D opportunities in the Government, Washington, DC
May 1997 (unpublished).

F.G.R. Hull, and C. Segami, Automated Knowledge Acquisition from
Encyclopedic Texts, IEEE International Journal of Artificial Intelligence
Tools v4 n3, pp. 349-367.

C.G. Interrante and F.J. Heymann (editors), STP 806 Standardization
of Technical Terminology: Principles and Practices, ASTM, 1983.

L.J. Kaetzel, Proposed Format for Data on Cements in a Materials
Properties Database, NIST Internal Report 6034, National Institute of
Standards and Technology, 1997.

L.J. Kaetzel, J.R. Clifton, P. Klieger, and K. Snyder, Highway Concrete
(HWYCON) Expert System User Reference and Enhancement Guide,
NISTIR 5184, National Institute of Standards and Technology,
Gaithersburg, MD, May 1993.

L.J. Kaetzel, J.R. Clifton, and K. Snyder, Users Guide to the Highway
Concrete (HWYCON) Expert System, SHRP-C-406, Strategic Highway
Research Program, National Research Council, Washington, DC,
1994.

B. A. Kipfer (editors), Roget’s 21 Century Thesaurus, Dell Publishing,
New York, New York, 1992.

Knowledge Systems Laboratory, Institute for Information Technology,
National Research Council Canada, Fuzzy CLIPS Version 6.02A
User’s Guide, September 1994.

R. Lederer, The Miracle of Language, Pocket Books, 1991

J. Mayfield, Y. Labrou, and T. Finin, Evaluation of KQML as an Agent
Communication Language in Intelligent Agents II: Agent Theories,
Architectures, and Languages, Springer-Verlag, New York, NY, 1995,
pp. 347-360.

T.E. Nagle, J.A. Nagle, L.L. Gerholz, and P.W. Eklund (editors),
Conceptual Structures Current Research and Practice, Ellis Horwood,
1992.

S. Nirenburg and V. Raskin, Ten Choices for Lexical Semantics,
Computing Research Laboratory, New Mexico State University,
MCCS-96-304, 1996.

H.S. Nwana, Software Agents: An Overview, The Knowledge
Engineering Review v11 n3, 1996.

J. Pustejovsky and S. Bergler (editors), Lexical Semantics and

23



terminology, and knowledge management components that would be developed and

made available through a pilot system.

Headings _| Crawl Phase Walk Phase Run Phase
Research e Software Agents ° Automated
Applications Knowledge
Acquisition
e Intelligent Agents
User Information Retrieval e Decision Support « Expert Systems
Support * Collaborative « Computer-based
Applications Environment training
Data Web-enabled database e Many standard data e Complex standard
« Standard metadata elements data models
° A few standard data e Simple standard data
elements models
» Complex data models
Documents |* Web-enabled document | Simple Standard « Complex standard
Management system (or Document Models document models
system of systems) (DTDs)
Terminology | * Standard terminology e Existing standard «Standard lexical
e Adapt existing lexical concepts model or ontology
models * Thesaurus
* Align differences in
selected lexical
models
Knowledge ° Standard knowledge
model
e Knowledge base

Table 3: Development Phases

An abbreviated example that addresses the application described in the opening
paragraph of section 4 would initially proceed as described below:

1.

The first step is to identify coating dictionaries as the standard for the terminology
that is used in the specific application area. If a coatings dictionary does not exist,
one could be developed (though this would be very time consuming).

The next step is to begin collecting machine-readable documents that address the
area of interest. The subjects for this example would include how to: assess the
condition of a coating, perform failure analysis, and make economic decisions when
the existing coating system must be repaired or replaced.

In parallel, decisions about how all of these documents will be managed (e.g., using
a file system, an existing database, or a dedicated document management system)
must be made and implemented. This would include selecting the web-enabled
database and document management system that will be used. If it is not possible
to select a single system or type of system for managing all documents, then it will
be necessary to develop specialized interfaces between different systems.

All documents should be contained within the document management system (or
system of systems) and in a format that permits searches using information retrieval

19



representation can be used for engineering simulation models, with deeper
representation based on fundamental theories.

32


natural-language processing for automated knowledge acquisition [HBCAD91, HS95],
should influence an organization's editing guidelines for document creation or revision.
Another long-range consideration is using Standard Generalized Markup Language
(SGML) [GR90] for embedding additional structural, semantic or other additional tags
for information retrieval or automated knowledge acquisition, and for those tags to apply
to both paper and hyperdocument formats.

Assuming that machine-readable documents are an organization’s major knowledge
assets, making a reasonable investment in an integrated Document Management
System (DMS) is justifiable. Typically, these documents include design guides,
handbooks, guides to practice, etc. A DMS may address storage, creation, version,
retrieval, access control, and routing of an organization’s documents. Frappaolo [20]
describes three types of DMS: file, library, and compound document managers.
Compound document managers treat documents as a collection of other documents. If
we imagine the typical desktop suite of office tools (word processing, spreadsheets,
databases, presentation, etc.) a compound document could include all of these
document types and more, using technology such as object linking and embedding
(OLE), Extensible Markup Language (XML) [W°-23], HTML, SGML, etc. Frappaolo
goes on to identify the extent of the collection as desktop, group, or enterprise-wide.
Evaluating W°-enabled enterprise-wide compound document manager products
provides insights into this technology. Among the vendors identified by Frappaolo are
Documentation, Inc. [W°-5] and Interleaf Inc. [W°-6], which provide broad coverage of
the technology. Open Text is an example of a quick growing W*-enabled DMS [AW97].

Finally, we will be investigating the DARPA, NASA and NSF funded Digital Library
Initiative [W°-7] as it relates to implementing a knowledge center for insight into how
machine-readable electronic documents could be managed for maximum utility. The
initiative’s focus is to research the means to collect, store, and organize information in
digital forms, and make it available for searching, retrieval, and processing via the W°
with a user-friendly interface.

3.3.3 Knowledge

One important objective is the identification of a neutral knowledge structure as an
interlingua or repository to enable knowledge exchange, sharing, or reuse. Currently,
the use of the DARPA-sponsored Knowledge Sharing Effort [PFPMFGN93] is being
evaluated. The DARPA effort includes the Knowledge Interchange Format (KIF),
Knowledge Query and Manipulation Language (KQML), and an Ontolingua server [W°-
12]. KIF is a first-order logic-based language with extensions, serving as an interlingua
between knowledge systems and as a meta-knowledge representation language. The
goal of KIF is to provide a thorough language for expressing knowledge while
minimizing the constraints of operational requirements. Stanford University, through its
Ontolingua server, [W°-13] has implemented a subset of KIF with extensions for use by
collaborating W° authors. However, there are other competing interlingua languages
such as conceptual graphs [NNGE92, PN93, W*-14], EXPRESS [Wils96], and higher-
order-languages (beyond first-order logic) [GM93]. KQML [MLF95], a language and
protocol for exchanging information and knowledge, is being evaluated by the research

14


2 External View

The external view is analogous to looking at the outside of a car. This view has been
divided into partners, user interface, and network.

2.1 Partners

Partners are organizations with whom we will collaborate. The three types of partner
organizations that we plan to work with in building this framework are knowledge
providers, user associations, and research organizations. Some organizations may
incorporate two or three areas. For example, a trade association (e.g., the Society for
Protective Coatings; American Concrete Institute) is a knowledge provider and
Tepresents users, while exploring internally how to deploy their knowledge. The three
categories help focus our collaborations.

Knowledge providers will become “Knowledge Centers” for specific domain areas.
These organizations must determine if it is in their best interest to become a national or
worldwide expert in their domain area. With that goal in place, they will be interested in
a framework, ultimately including electronic commerce, that helps manage and
disseminate their knowledge. Standards, trade, and private organizations are typical
organizations that are in different stages of incorporating the CIKS framework.

User organizations that will be identified as potential partners include those that are
concerned with facility operations and maintenance. We would look to these
organizations to help guide knowledge presentation, and to determine an appropriate
user interface. These organizations could help us focus on additional areas that would
help their constituents.

Finally, research organizations will provide reusable information technology research
results that could be incorporated into the framework. These include educational,
government, and private organizations with an interest in research that is freely
available to the public. Typically, government funded research programs at universities
are of interest since the intellectual property is normally public and available on the W°.

2.2 User Interface

Using the premise that knowledge repositories will be distributed and accessible from
knowledge centers, the design and implementation of a coherent user interface will be
challenging. One of the major challenges is usability. Several approaches will be
taken, which include considering the user's context, providing a natural-language
interface, and/or providing an application software based approach.

The context-based approach would consider a number of perspectives. The following
three have been identified:


principles of terminology management addressing concept representation, description,
and systems. This book provides a strong case for pursuing the representation and
management of concepts as critical for addressing the lexicon, terms, or words used in
natural language.

A.4 Document Models

Most print document models discuss the logical as opposed to the physical structure.
The logical structure includes elements like chapters, sections, paragraphs, sentences,
words, and characters. The physical structure primarily involves the placement of
elements on the page. Most of the models are involved in converting the logical
structure to the physical. Our interest is primarily in the logical structure, not the
translation to the physical.

Furuta [AFQ89] identified element types, structures, constraints, relationship types, and
homogeneity as distinguishing criteria in describing other document models. Element
types refers to the kind of element (e.g., text, math formulas, tables, or graphs) and
level of detail (e.g., paragraph, sentence, word, character, or some sub-part of a
character) that they handle. Structure refers to a tree or acyclic graph, and within those
structures, there may be constraints to specify the relationships between elements. In
terms of relationship types, there may be predominant ones between major elements,
and secondary relationships for references or footnotes elements. Finally, there are
homogeneous or heterogeneous relationships between element types or within
elements. Within this setting, Furuta identified a range of document models from the
simple linear to the flat pseudo-hierarchical, as well as environmental with limited
nesting, and more capable models such as unconstrained tree-like, constrained
heterogeneous tree-like, and truly hierarchical.

Joloboff [AFQ89] discussed the evolution of the document representation models. The
first generation involved improving the process of going from the revisable form that the
author used to the final form that was printed. The second generation (which includes
SGML) provided for richer typographical options, used logical and hierarchical
structures, used relationships between elements, and extended element types (e.g.,
math formulas, tables, references, indexes, etc.). The third generation moved toward
the direct manipulation of the elements and structure (WYSIWYG) without knowledge of
a particular internal representation. Joloboff identifies Xerox’s Interscript as a third
generation model. A major goal for Interscript was to have the ability to exchange
documents between different systems with different abilities, without needing to know
the internal structures of the other system. The approach was similar to a programming
language that is ported from one platform to another. Interscript had a script or program
to reconstruct the internal representation on any platform. However, this made it more
complex, and therefore, difficult to review by normal users. It was also designed so that
the attributes of an element are not static, but are resolved dynamically within context
as elements are moved around.

29



Appendix A: Conceptual Representation View

This section focuses on the conceptual representation research background,
documents some of the research, and points the reader to relevant references. It is not,
however, intended to be a complete survey of the field. The framework was assembled
with an implementation bias and with consideration of a reasonable representational
foundation. Included in this section are: background information, discussion about data,
term and concept, document, and knowledge representation models.

A.1 Background

It is thought that natural language is the root or foundation upon which we build formal
models and that better understanding of natural language problems will provide insight
into building better formal models. Relevant work from the computational lexicography
field discusses research using machine-readable dictionaries. One study by Wilks et al.
[BB89] analyzed a small dictionary for learners of English as a second language,
Longman's Dictionary of Contemporary English (_.DOCE). The preparers of LDOCE
stated that they used a controlled vocabulary of about two thousand words. This
controlled vocabulary was used to define all of the other words, and there were on
average twelve different meanings for each word. In that study, one of the researchers
identified a set of about twelve hundred words called the “Key Defining Vocabulary”
which were found to define the two thousand word controlled vocabulary. What remains
to define those twelve hundred words are only themselves. Another study conducted by
Professor Ohmann at Wesleyan University [Lede91] provides insight into the richness
of natural language. In this study, he asked twenty-five students to describe, in one
sentence, a single cartoon. He then collected the twenty-five sentences and
determined that he could create twenty billion grammatical sentences, just with the
words that were used in the initial set. This is an example of the complexity of natural
language, and it points to the challenge involved in creating our models.

Models are used as a substitute for what exists in a natural or abstract world. No model
can replicate the infinite complexity of our natural world; therefore, no model is
complete. Therefore, we must accept that the consequences of imperfect models are
imperfect results [DSS93]. As we automate knowledge-like activities involving expert
systems and intelligent agents, the computer representation may become more
complex and not easily understood by people used to using only natural language. The
benefit of this complexity is the ability to automate what previously required human
participation. Currently, although computers have problems with processing natural
language, they are improving. Perhaps some day in the far future we will be able to
converse with a computer. However, for now, we need to build formal models based on
formal representations.

26


Additional knowledge formats will be included in the CIKS framework. These include,
data from material tests, and condition assessment (both human and machine-
readable), visual information, facts and rules-of-thumb from domain experts. Table 1
provides examples of CIKS knowledge formats, examples, and knowledge sources.
The challenge brought by the use of multiple knowledge formats involves the need to
integrate information to support the decision-making process. Just-in-time delivery of
information, coupled with "when-changed" and "when-needed” actions are important
considerations. An example of a "when-changed" action can occur when new data,
information, and knowledge is created (i.e., a coating formulation has changed that
affects its performance). A “when-needed” action occurs when information is needed to
make an informed decision, such as detailed information for better understanding or to
substantiate a computer-generated hypothesis, conclusion, or recommendation.

The ability to integrate and represent the Table 1 formats will depend largely on the
success of establishing common terms, dictionaries, and standard guides for
representation. Also, criteria must be established that reflect data integrity or its
absence.

Commercial development and implementation tools

An evaluation of commercial off-the-shelf (COTS) products and information technology
research will be conducted with an emphasis on reusability. This will accelerate the
development process and reduce the level of effort needed to implement CIKS. The
intention is not to conduct new research, but to integrate or ‘glue’ existing information
technologies and research results into a functional framework. If voids exist, then the
value of building or researching those missing links will be considered.

Development and implementation partnerships

Private and public knowledge centers and digital information networks with specific
focus areas (e.g., coating, high-performance concrete) hosting their knowledge
repository will serve as examples of the CIKS framework. Industry leaders will
collaborate as partners to establish and host knowledge bases, identify user
requirements, and provide reusable research results. One factor involved in the
success of CIKS will be industry's interest and ability to adopt CIKS strategies and, in
some cases, reengineer their business process. User demands for value added
knowledge in digital form will serve as a driver for product and material manufacturers
and information brokers to maintain a competitive advantage. Upon completion, the
various stages for implementing the CIKS framework, will be submitted to a standards
setting body for standardization. Two scenarios can be presented for possible
standardization: 1) a U.S. national body such as ASTM, or 2) a domain specific body
such as The Society for Protective Coatings for the coating industry or the American
Concrete Institute for cementitious materials. The standardized framework would
establish standards and guidance on terminology, data formats, and models for


Goals of CIKS

Universal exchange of knowledge

One goal is to help the construction industry share, exchange, and manage knowledge
through a neutral knowledge interchange format. A major focus will involve document-
based knowledge sources such as handbooks, guides, standards, and dictionaries.
These documents provide a clear reference to knowledge sources that have been
refereed by industry leaders. Examples of these include the American Society for
Testing and Materials (ASTM), the Society for Protective Coatings (SSPC), and the
American Concrete Institute (ACI). Use of guides developed by the public sector will
result in the creation of digital libraries, and decision-support aids (e.g., expert systems)
that will be available to knowledge users, such as practicing engineers. Commercial
guides will be incorporated into the CIKS framework by: 1) an interpretation of the
guides (contained within an expert system) to aid in engineering practice, and 2) the
application of electronic commerce, permitting the sale of documents in electronic form.

Knowledge Example of use Knowledge Examples
format source
Facts and rules- | Represented in Domain experts, HWYCON expert system
of-thumb decision-support standards
functions by a organizations,
reasoning process, | guides to practice
resulting ina
computer-generated
hypothesis,
conclusion, or
recommendation.
Documents: Form digital libraries | ASTM, SSPC, ASTM C-150 "Standard
standards that can be ACI, AASHTO, Specification for Portland
describing test retrieved, viewed, public works Cement", SSPC "Good
methods, and interpreted by a | facilities, product Painting Practice", ACI
material knowledge user. and material "Manual of Concrete
specifications, manufacturers and | Practice"
guides to suppliers.
practice, results
of relevant
domain specific
work
Data Entities (databases, | Product and Product data sheets,
spreadsheets, material supplier corrosion resistance data
photographs, performance sets from accelerated aging
audio/video clips) measurements, tests, photographs of
that reflect results or | materials research | material failures and
provide facts. organizations. conditions.

Table 1:

CIKS knowledge formats, examples, and knowledge sources.


community to interface with other distributed knowledge bases, and will be tested for
applicability in the software agent areas (see section 3.1.2). For these reasons, the
DARPA effort is being considered as a practical choice to evaluate at this time.

A maior effort will be required to integrate existing knowledge bases. In order to
accelerate the construction of a building materials knowledge base, it will be necessary
to incorporate existing knowledge bases in these and other related efforts.
Consideration may be given to building translators from native formats that the existing
knowledge base is in, to a neutral knowledge interchange format such as KIF or a KIF-
like structure. The long-term goal is to develop sharable knowledge to demonstrate the
concept, initially by creating a central repository and then by identifying solutions to
using distributed knowledge bases. However, it must be said that KIF only provides the
syntactical solution, not the semantics. The semantics will have to be built using tokens
embedded in a KIF or KIF-like structure.

15



implementation of data, document, and knowledge exchange mechanisms (through
standard models) and its role as the foundation upon which all of the other models are
built. The current exchange models provide only a syntactical exchange mechanism,
while the lexical semantic model provides the foundation for a much more powerful
semantic exchange mechanism. However, the intention is not to conduct natural
language research, but to incorporate the research results into a functional framework.

Natural Language to exchange mechanisms

Dictionary,
Thesaurus &
Corpora
Concept Concept
Terms Concepts Definitions | _ [Relationships

Figure 2



5 Summary

This report provides an overview of a proposed framework for CIKS that addresses the
area of construction industry products, materials, components, and systems. The intent
is to provide an implementation solution, which can add value in the short-term, yet is
flexible enough to include longer-term information technology research results. It is the
authors’ intent that this report provide the basis for a blueprint to facilitate exchanging
and sharing knowledge for the construction industry. The framework is considered a
master plan for the CIKS effort, and therefore will be adjusted to reflect the changing
needs of knowledge users and new information technologies. The long-term goal of
CIKS is to establish a standard framework for knowledge sharing within the construction
industry. Short-term results will involve developing domain specific applications that
address specific components (e.g., data formats, digital libraries, decision-support
modules).

The framework's foundation is built on standard terminology, which should be used to
define standard data elements, formats, and know-how. Standard terminology requires
agreeing on dictionaries, thesauruses, and keyword lists, which are written in natural-
language. An analysis of natural-language is critical to understand the natural-language
issues in data and knowledge sharing that may resurface when data and knowledge is
exchanged using formal data or knowledge languages (e.g., SQL, KIF, etc.). A model
of the terminology (e.g., lexical semantic model, ontology, or lexical knowledge base) is
critical to illustrate issues, obtain consensus, and help create a bridge between our
natural and formal worlds and their respective languages.

An indicator of the success of this framework will be its adoption by construction
materials organizations that will test the framework in the conduct of their business or
practice. The authors are available to consult with organizations which endeavor to
implement a knowledge management strategy, and to work with user organizations to
help ensure that the framework meets their needs.

Managing an organization’s knowledge requires a major effort from the basic methods
used by libraries (as the original knowledge repository) to the development of a
knowledge management framework. This framework provides a phased approach for
an evolutionary process, from developing a digital document library to completing an
agent-enabled distributed knowledge base.

Finally, the framework will continually evolve and improve as new research is made

available. The authors encourage feedback on CIKS strategies, potential partnerships,
and applications.

21



NISTIR 6071

Computer Integrated Knowledge System (CIKS)
for Construction Materials, Components, and
Systems: Proposed Framework

Thomas Y. Kurihara
Lawrence J. Kaetzel

Building and Fire Research Laboratory
Gaithersburg, Maryland 20899

NST

United States Department of Commerce
Technology Administration
National Institute of Standards and Technology


Selected Bibliography of Knowledge-Based Systems

H. Adeli (editor), Expert Systems in Construction and Structural Engineering, Chapman
and Hall, New York, NY (1988).

C. Arnold, R. Drisko, J. Griffith, J. Neal, T. Nguyen, and J. Yanez, An Expert System
Application for Paints and Coatings: Painting Advisor, Proceedings of SSPC '87, SSPC
87-07, pp. 1-6, Steel Structures Painting Council, Pittsburgh, PA (1987).

S.K. Boocock and L.J. Kaetzel, Coating Industry Knowledge Base Systems: An
Introduction to the SSPC Knowledge Center and the NIST Computer Integrated
Knowledge Systems Network, Proceedings of SSPC ’96, pp. 144-149, Steel Structures
Painting Council, Pittsburgh, PA (November 1996).

J.R. Clifton and L.J. Kaetzel, Expert Systems in Concrete Construction, Concrete
International, American Concrete Institute, Farmington Hills, MI (November 1988).

L.F. Cohn and R.A. Harris, Knowledge Based Expert Systems in Transportation: A
Synthesis of Highway Practice, National Cooperative Highway Research Program
Synthesis 183, Transportation Research Board, Washington, DC (September 1992).

Federal Highway Administration, Developing Expert Systems, Publication No. FHWA-
TS-88-022, U.S. Department of Transportation, McLean, VA (December 1988).

L.J. Kaetzel and J.R. Clifton, Expert/Knowledge-Based Systems for Cement and
Concrete: State-of-the-Art Report, Strategic Highway Research Program, SHRP-
C/UWP-91-527, Transportation Research Board, Washington, DC (1991).

L.J. Kaetzel, J.R. Clifton, P. Kleiger, and K. Snyder, Highway Concrete (HWYCON)
Expert System User Reference and Enhancement Guide, NIST Internal Report 5194,
National Institute of Standards and Technology, Gaithersburg, MD (May 1993).

L.J. Kaetzel, J.R. Clifton, K. Snyder, and P. Kleiger, Users Guide to the Highway
Concrete (HWYCON) Expert Systems, Strategic Highway Research Program, SHRP.
C-406, Transportation Research Board, Washington, DC (1994).

L.J. Kaetzel and J.R. Clifton, Expert Systems for Building Materials and Structures:
Expert/Knowledge Based Systems for Materials in the Construction Industry, RILEM
Journal of Materials and Structures, Volume 28, pp. 160-174, RILEM (1995).

L.J. Kaetzel and M.E. McKnight, Enhancing Coatings Diagnostics, Selection, and Use
Through Computer Based Knowledge Systems, Proceedings of SSPC 95, SSPC 95-
09, pp. 287-295, Steel Structures Painting Council, Pittsburgh, PA (November 1995).

S.S. Kuo, D.A. Clark, and R. Kerr, Complete Package for Computer Automated Bridge
Inspection Process, Transportation Research Board, Washington, DC (1988).

44


3 Management View

The management view is a top-down view of the major functional components, divided
into application, data, and knowledge management, with each component having
additional sub-elements. The distinction between data and knowledge is tenuous.
Dictionaries appear to be circular in their definitions of data and knowledge. For our
discussion, data is what users store in commercial databases, though some databases
could store all of the knowledge management sub-elements. What distinguishes typical
data from knowledge in this report, is the ability to inference. Data in this sense is more
static, while knowledge is more dynamic.

3.1 Application Management

This section is divided into the user-oriented and the research-oriented applications.
Both application types will interface with the data and knowledge management
components discussed in the next sections.

3.1.1 User applications

The purpose of this section is to identify tools or technologies to help users improve the
performance of work tasks using commercial products. These include information
retrieval systems, collaborative environments, computer-based training systems, expert
systems, or any other available technology that would improve a user’s performance
within the context of the framework.

An appreciation of the state-of-the-art in information retrieval can be acquired by
searching for content on the W° using search engines such as AltaVista, Excite, and
Yahoo. Commercial information retrieval products can be obtained from major software
vendors like Fulcrum [W°-24], Microsoft [W°-17], Oracle [W°-18], Verity [W°-25], and
many others. Documents may be divided into “information or knowledge chunks” to
provide the relevant and appropriate level of detail and avoid retrieving a book when
only a paragraph or page is needed.

The goal of using collaborative software is to overcome space and time limitations in
order to enable people in different locations and time zones to work together as a virtual
organization. Email is a common application, which allows collaboration with others at
different locations and different times. Using collaborative technology to allow
knowledge workers to collaborate at different locations during different times (e.g. when
creating or revising a knowledge document) will reduce the time to completion of a task.
Document Management Systems (DMS), discussed in section 3.3.2, should provide the
capability to perform collaborative document processing, as will tools from major
software vendors that allow one to collaborate across the Internet. A recent Seybold
Report [Eccl97] identified a product that “allows authors and editors to enter and edit
content through web browsers.” Finally, national efforts have been the focus of
attention at workshops such as the “IEEE Sixth Workshop on Enabling Technologies:


e Product life-cycle: planning, design, construction, operations, maintenance and de-
commissioning;
Technical discipline: architectural, civil, mechanical, electrical, materials, etc.;
Industry: commercial buildings, transportation, process plants, ship building, etc.

By reviewing well-known document interfaces (e.g., Sweets or Thomas Register) used
in a specific context, a familiar user interface could be provided, and modified by the
user. However, the copyright issues within the user interface area must be considered.

A general natural-language based approach would be a challenging undertaking.
However, in the near future, we may have an adequate system for attempting to answer
simple, very constrained natural-language questions.

Another approach is application software based, with similar application types having
similar interfaces. For example, if all expert systems built in this framework used the
same product, the interface would be the same assuming the same design specification
was applied.

Accurate assessment of user needs will facilitate building customizable user interfaces
to handle the distributed knowledge bases. By reviewing commercial vendor products
and results from the computer science research community, insight into which enabling
information technologies should be considered for implementation will be provided.
Areas such as natural-language processing, virtual reality, and distance learning will
provide additional insights into how to design a robust user interface. CIKS material
and information technology working groups, established to develop pilot projects for
their respective areas, will test the feasibility and usefulness of the CIKS user interface.
Finally, providing help systems or computer-based training will bridge the gap during the
transition stage from an evolving user interface to one that is highly usable.

2.3 Network

The network is the digital communication infrastructure that enables users to connect to
knowledge bases hosted at knowledge centers. The W* will provide the major
networking infrastructure. Where possible, we will focus on the “non-networking”
aspects of the framework and adapt to the particular “networking de jour’ since
information technology product life cycles are getting shorter. For example, a
commercial software vendor, Citrix Incorporated, has developed the WinFrame client-
server product that allows one to run WindowsTM applications using the W°. Use of this
capability simplifies Porting of CIKS applications from a WindowsTM client-server
environment to a totally W° integrated application environment.


reduce software development and maintenance costs. Access to databases can be
provided in a more timely and convenient manner. Figure B-5 contains a diagram of the
components of a distributed technical database system designed for W’ access. This
model can be applied to virtually any type of database. The client (user) is provided the
functionality of submitting queries (questions) to the database in an interactive mode.
Typically, this is done using a W* browser program. The process of converting input
from the client involves converting the query into a SQL statement. In the instance
shown in Figure B-6, this is accomplished using the ISO Standard 9579 [BS96],
“Remote Data Access” (RDA). This standard is a generic model providing database
access and has been implemented at NIST for interfacing W* clients to SQL databases.
The RDA standard was implemented using the C Programming Language. After
receiving the SQL statement, the database management system retrieves the data from
the physical database and produces a table containing the data elements (fields) and
their values. This information is returned to the RDA component that formats the
information for output in the Hyptertext Markup Language (HTML) and displays the
information using the W® client's browser. Added capabilities have been developed to
also produce graphical plots. An interface has been developed to the NIST Dataplot
Statistical and Graphical Analysis system [Fill84].

Smart Interface:
Cost analysis,
Database service-life prediction (“>
interface (e.g.
Query RDA, Fig 7) t

Coating WWW client or

performance

Perec, | Ron-netwarked use

Technical
databases on.
coating
material

Case specific
database
subset or view

Results

HTML Graph
formatted
‘ubte

Data sources

y Formatted | ]
Database SQL (eg.
scheme . “ee hl
Uniform
Laborato database formats WW cli
ora ry, pperdaiictng WWW client
field tests
‘+ properties:
* product data

Figure B-5: W* implementation for technical databases.

This permits the graphical display of database information, interactively. Since the
construction industry is comprised of companies with varying degrees of personnel and
funding resources. It is necessary to test knowledge system development using
multiple platforms. Figure B-6 shows a method can be used in the CIKS test bed to

40


tules with limited scope; the relation between static (context-independent) and dynamic
resources; the commitment to descriptive coverage; the trade-off between generalization and
idiosyncrasy; and, finally the adherence to the method-oriented or task oriented ideology of
research. The discussion is inspired by, but not limited to, the comparison between the
generative lexicon approach and the ontological semantic approach to lexical semantics.

Historically, generative has meant “a way of enumeration through mathematical
derivation,” and has been characterized as "world knowledge." The major points of the
generative lexicon (as Nirenburg and Raskin referenced Pustejovsky) are that some of
the lexical item meanings are characterized in relationships with other lexical items.
These relationships result in fewer senses for a lexical item, and the missing senses
can be derived with lexical rules operating on the identified relationships with other
lexical items. Most common dictionaries contain enumerated lexicons and they have
been criticized for lacking relations, inconsistent sense selection criteria, and being
incomplete in usage. Enumerated lexicons can be as good as generative lexicons
provided they use theory-based procedures and methods.

Other researchers believe that ontologies are an essential part of lexical semantics.
Nirenburg and Raskin quote Pustejovsky [NR96], who said, “the meanings of words
should somehow reflect the deeper conceptual structures in the cognitive system, and
the domain it operates in”. Furthermore, the authors state that "the notational elements
that are treated as theory within the generative lexicon approach can, in fact, be
legitimately considered as elements of semantic theory if they are anchored in a well-
designed conceptual ontology. Then and only then, can one justify the postulation of a
certain number of theoretical concepts, a certain set of roles and features, and a
prescribed range of values.” Other researchers characterize these ontological efforts as
creating another natural-like language to manage the real natural language while
asserting its necessity. The reply of Nirenburg and Raskin to that accusation is that
they are building a “language-neutral” representation for some domain, which includes a
semantic primitive repository with a semantic and “discourse-pragmatic’” relational
network between primitives. The ontology’s function is to provide the context or world-
knowledge for natural language processes. Unfortunately, ontologies are expensive to
construct, maintain, and reproduce when they are hand-built. Automated methods
should be used to help ensure consistency and the leveraging of intellectual capital for
teuse in other domains. The ontology research area deserves attention because it is
believed to be a foundation to enable knowledge sharing, software agents, and
automated knowledge acquisition. For this reason, CIKS will track relevant
organizations (e.g., AAAI) which hold conferences on ontologies in order to continue
evaluating their usefulness.

The previous discussion was predominantly from the artificial intelligence and
computational linguistic fields. Shifting to the terminology field is like moving from the
research to the engineering perspective for doing near-term work. Terminologists can
be found in the translation or technical language areas (e.g., ASTM), and therefore,
appear to be more pragmatic. The terminology community focuses on the importance
of concepts as indicated in several sources [WS95, SW93, and !H83]. In addition, the
1997 Handbook of Terminology Management [WB97] begins with the fundamental

28



O.S. Marshall, Jr., Development of the PAINTER Engineered Management System,
U.S. Army Corp’s of Engineers, Construction Engineering Research Laboratory,
USACERL Special Report FM-94, Construction Engineering Research Laboratory,
Champaign, IL (January 1994).

J.H. Pielert and L.J. Kaetzel, Cement and Concrete Materials Databases and the Need
for Quality Testing, Materials Science of Concrete Ill, pp. 337-358, American Ceramic
Society, Westerville, OH (1992).

R. Sharpe, B.S. Marksjo, F. Ho, and J.D. Holmes, WINDLOADER: Wind Loads on
Structures Advisor, Building Construction Engineering, SP-012, CSIRO Division,
Australia (July 1989).

U.S. Army Corp’s of Engineers, Construction Engineering Research Laboratory,
Knowledge Acquisition for Expert Systems in Construction, USACERL Special Report
M-89/10, Construction Engineering Research Laboratory, Champaign, IL (September
1988).

45


A.2 Data Models

This section refers to the STEP (ISO 10303) community to provide a data modeling
methodology. There are many alternative representations such as entity-relationship,
IDEF1X, NIAM, OMT, Shlaer-Mellor, DAPLEX, GEM, SQL, EXPRESS-G, and
EXPRESS [SW94]. STEP has been active in the data representation area and there
are many commercially available data modeling products for interested parties to review
for more information. Due to the accessibility of information and coverage of data
representation by STEP and the commercial community, CIKS will look to these existing
efforts for guidance in the data modeling area.

As mentioned earlier, the terms, concepts, documents and knowledge can be
represented in a data model and stored inside a database. This report has made the
distinction to focus our discussion on the knowledge management aspects. What
distinguishes typical data from knowledge in this report is the ability to inference. Data
in this sense is more static, while knowledge is more dynamic.

A.3 Term and Concept (Lexical Semantic) Models

The Handbook of Artificial Intelligence [BF81] discusses semantic primitives within the
knowledge representation chapter, and characterizes them as the building blocks that
provide meaning. Wilk's research, cited in that chapter, identified recommended
properties (finite, comprehensive, independent, noncircular, and primitive) to develop a
set of primitives. This was contrasted with Shank’s Conceptual Dependency Theory,
which is based upon concepts, instead of words. Shank claimed his representation
should be language independent, assuming the set of unique concepts was identified.
However, in an environment of syntactic and semantic ambiguity, his concept
representation required an unambiguous and unique representation. Both ideas appear
similar in that ultimately, senses or concepts need to be identified.

Initial work on dictionaries [Ams|81] provided insight into using computational methods
to identify the tangled semantic relationships between lexical items contained in a
machine-readable dictionary. This effort and others resulted in development of the field
of computational lexicography. Other examples of research which provide background
information for modeling lexical semantics are contained in Relational Models of the
Lexicon [Evens88], Computational Lexicography for Natural Language Processing
[BB89] and Lexical Semantics and Knowledge Representation [PB92].

One view of the options is provided in a recent paper entitled, “Ten Choices for Lexical
Semantics,” by Nirenburg and Raskin [NR96]. The abstract states:

The modern computational lexical semantics reached a point in its development when it has
become necessary to define the premises and goals of each of its several current trends. This
paper proposes ten choices in terms of which these premises and goals can be discussed. It
argues that the central questions include the use of lexical rules for generating word senses; the
role of syntax, pragmatics, and formal semantics in the specification of lexical meaning; the use of
a world model or ontology, as the organizing principle for lexical-semantic descriptions; the use of

27


representing processes (e.g., condition assessment, and material selection procedures)
in decision-support systems.

CIKS Proposed Framework

Contained in this report are several perspectives, which include the external,
management, and operational views. Section 2, the external view, includes the
partners, user interface, and network. Section 3, the management view, describes the
application, data, and knowledge management. Section 4, an operational view,
describes the pilot system, menu of options, and the different development phases, and
outlines the steps for building an example of the framework. Section 5 provides the
summary. Appendix A, a conceptual representation view, describes exchange options,
Appendix B provides a description of how the CIKS framework could be implemented
for a CIKS coating partnership for highway structures, and Appendix C is a brief
glossary.

Figure 1 shows the different views and their respective parts. The foundation of the
framework is built on the conceptual representation which includes dictionaries, a lexical
semantic model (a model of the relationships contained in the dictionary), standard
terms, data elements, data models, document models, and knowledge models. All of
these items will be discussed in their respective sections. (The square shapes refer to
formal or engineered items and the curved shapes refer to natural or human aspects.)

Proposed Framework.
C Partners ») E ;
C User Interface _) ae
| (WWW) Network | lew
Application : Management
Data and Knowledge View
Pilot System Operational
—_ ( Options & Phases View
Standard (, ») Docu-\| Know-
Terms &\| Data | Data
Exchange |7>7 onl (Elements|Models| fodels [Midis  CONCeptual
"Y_ Eexical Se ic Model Representation
x fatural (eg. model of the terminology ) +) View
nalysis Dictionaries, thesauruses, keywords,
cssneneeneseoneoson indexes, and corpora

Figure 1

Figure 2 shows that dictionaries, thesauruses, and corpora or machine-readable text
are the inputs to creating the lexical semantic model, a foundation of the framework.
This figure also shows the relationship of the lexical semantic model to the


Headings Knowledge Management
Data Terms and Documents | Knowledge
Concepts
1. Primary « Domain Experts | Documents Documents Documents
real-world e Dictionaries * Handbooks « Handbooks
sources e  Thesauruses *° Guides * Guides
«Keywords e Standards «Standards
2. Example e STEP e ASTMEO2 e SGML « DARPA
standards, e ASTM E49 e ISOTC 37 community Knowledge
research ° DAMA e Terminology « ODMA Sharing
communities |. ODMG © Computational | Digital Library | * AAAI
or projects e Metadata Linguistics Initiative IEEE
e° IEEE « UMLS « Document
Management
3. Conceptual | * ERD ° Primitives Print-based ¢ First Order Logic
representation |e IDEF1X * Concepts ¢ Logical ° Frames,
alternatives e =NIAM * Ontology ° Physical semantic nets,
e OMT Hyperdocument production rules
« EXPRESS-G « HAM e Neural Nets
e Dexter e Uncertainty &
e_ Tower Fuzziness
4. Exchange e Data Elements e = KIF «Proprietary e =KIF
or Sharing e SQL e EXPRESS « SGMLDTDs |« EXPRESS
options « ODL/ODQ e Conceptual e XML e Conceptual
« EXPRESS Graphs e (ODA) Graphs
« (Middleware) ° TIF (aDTD)
5. * CASE tools © = Ontolingua e SGML and «= Ontolingua
Development |e Database and XML tools.
tools — procedural
language
extensions
6. Operations | Commercial Commercial Commercial Commercial expert
database database document systems
management management management
systems system system

Table 2: Menu of Options

4.3 Development Phases

This section shows a general implementation and the different phases of the CIKS
framework. After the material coating has been taken through all of the phases, other
materials will be considered to identify the issues of integrating heterogeneous materials

(e.g., concrete, steel) or components and systems (e.g., roofing, HVAC).

In Table 3, the column headings identify the different development phases. The column
headings of crawl, walk, and run phases are used to represent a progression. The rows
identify the type of research applications, user applications, data, documents,

18



essential for a successful product and to return the development investment. Often
knowledge content is the driving force for these systems. The Journal of Protective
Coatings & Linings, "JPCL Archives II" is an example of such a product. Future
versions of this product could be made available on-line via the Internet, while still
providing income to the publisher/distributor. Mechanisms currently being developed to
allow electronic commerce and intellectual property distribution on the W” will accelerate
this method.

The Internet W° has altered thinking on how an organization's knowledge is maintained
and distributed. For example, using an Intranet (an internal W*), access to knowledge
can be provided to personnel within an organization (e.g., marketing, testing, and
research departments). Use of an Intranet results in benefits such as shorter
development times, lower costs, and better data security. Although Intranets are useful
within organizations the maturation of new capabilities such as electronic commerce
and increased network traffic capacity will enable the Internet to support new
relationships among different organizations (e.g., agile virtual enterprises). To remain
competitive, Internet-based information is no longer a convenience, but a necessity.

Perhaps the most significant constraint affecting the distribution of organizational and
commercial knowledge-based systems is the lack of interoperability. Quite simply,
interoperability means the ability to use knowledge and software among computerized
systems. Incompatible data formats and computer hardware and software, incomplete
or subjective data, inconsistent terminology, and the lack of electronic access are
examples of specific factors that prohibit widespread use. Solving these problems will
require collaboration by industry and government. Agreement must be reached on a
common terminology, standard knowledge formats, criteria for establishing data quality,
and common computer interfaces that provide seamless integration of knowledge to the
user. The CIKS activity will address these issues.

B.3 Steel Structures Painting Council/Computer-Integrated Knowledge
System (SSPC/CIKS) Joint Coating Working Group

One group that is currently addressing coating industry knowledge issues is the
SSPC/CIKS Joint Coating Working Group. The Working Group comprises members
from the SSPC Committee C.4.10 on "Knowledge-Base Systems for Coatings," and a
CIKS Coating Industry Working Group formed during the CIKS June 1996 workshop.
SSPC's Executive Director, Bernard Appleman, chairs the group. Members of the
group include public and private sector organizations representing coating formulators,
consultants and engineers, facility owners, and researchers. Figure B-2 shows a
diagram of the group's interaction with the CIKS test bed and its role in the development
of CIKS.

35



SSPC/CIKS
Coating Working

Group
Pilot projects, process
and data models
CIKS Test bed Coati
z 7 ‘eating
Domain models terminology,
« Prototype development standards,
+ Infrastructure and guides
user interfaces
Knowledge Sharing y
Coating Industry and
Knowledge Standards Committees
Centers >| (SSPC C.4.10,
cn os 5 ASTM D.1, E.49
(Private and Public) y

Figure B-2: SSPC/CIKS Coating Working Group role in CIKS development.

Collaboration with other industry and standards setting committees such as ASTM
Committees D01 on "Paint and Related Coatings, Materials and Applications, " E02 on
"Terminology," and E49 on “Computerization of Material and Chemical Property Data”
will be necessary. These collaborations will result in consistent terminology and
standards for identifying, representing, and sharing coating material knowledge.

Specific focus areas of the SSPC/CIKS Joint Coating Working Group include:

e developing guides to assist coating industry knowledge users in using and
integrating the Internet in business practice;

« improving the communication of computer-based information such as messages,
computer-stored files, and access to application systems;
developing standard formats for representing and exchanging coating product data;
developing state-of-the-art reports describing current practice and enabling
information technologies that have application within the coating industry.

Future projects requiring longer lead-times (2-years) to implement include the
development of case-based reasoning (decision-making based on documented
observations of coating performance), data dictionaries, and expert systems. Products
from the Working Group will be disseminated in the form of SSPC Technology Updates,
Guides, and through the SSPC Coating Knowledge Center. The draft “Guide for the
Identification and Use of Industrial Coating Material in Computerized Product
Databases" exemplifies the Working Group's effort to develop standard guides and
methods for representing coating material knowledge. Table B-1 shows examples of
the data elements proposed in the guide.

36


[Penn93]

[PFPMFGN93]

[PN93]
[Seac96]
[Srir95]

[StCI96]
[Sw93]

[swo4]
[WB97]
[Wils96]
[ws95]

[w*-1]
[W°-2]
[w*-3]
[w*-4]
RS]
[w*-6]
[w-7]
[W°-8]
3_g]
[w?-10]
[W?-11]
[W?-12]
[w*-13]
[W*-14]
[W2-15]
[W°-16]
[w*-17]

Knowledge Representation, Springer-Verlag, 1992.

W.D. Penniman, Electronic Libraries and the Engineering Profession,
American Society for Engineering Education Annual Conference
Proceedings, vl, 1993, pp. 889-894.

R.S. Patil, R.E. Fikes, P.F. Patel-Schneider, D. Mckay, T. Finin, T.R.
Gruber, and R. Neches, The DARPA Knowledge Sharing Effort:
Progress Report in B. Neble, C. Rich and W. Swartout, (editors),
Principles of Knowledge Representation and Reasoning, Morgan
Kaufmann, 1992, pp. 777-788.

H.D. Pfeiffer and T.E. Nagle (editors), Conceptual Structures: Theory
and Implementation, Springer-Verlag, 1993.

D. Seachrist, How Multimedia Tools Compare, Byte Vol 21 No. 11,
November 1996.

R.D. Sriram, Intelligent Systems for Engineering: A Knowledge-Based
Approach, 1995 (to be published).

G. St. Clair (editor), InfoManage: The International Management
Newsletter for the Information Systems Professional, v3, n12,
November 1996.

R.A. Strehlow and S.E. Wright (editors), STP 1166 Standardizing
Terminology for Better Communication: Practice, Applied Theory, and
Results, ASTM, 1993. .

D. Schenck and P. Wilson, information Modeling the EXPRESS Way,
Oxford University Press, 1994.

S.E. Wright and G. Budin, Handbook of Terminology Management,
John Benjamins Publishing Company, 1997.

P.R. Wilson, On the Transtlation of Kif/Frame Ontologies to EXPRESS,
NISTIR 5927, 1996.

S.E. Wright and R.A. Strehlow (editors), STP 1223 Standardizing and
Harmonizing Terminology: Theory and Practice, ASTM, 1995.
Http:/Avww. ciks.nist.gov

Http://ssed 1 .ncr.disa.mil/srp/datadmn.html
Http://Awww.nlm.nih.gov/pubs/factsheets/umis.html

Http://www. jula.upf.es/riterm/ritxinuk.htm
Http:/Awww.documentum.com

Http://www. interleaf.com

Http://www. cise.nsf.gov/iris/DLHome.html
Http:/Awww.asymetrix.com/products/toolbook2
Http://www.jsc.nasa.gov/~clips/CLIPS.html
Http://herzberg.ca.sandia.gov/jess

Http://www.cs.umbc.edu/kse

Http:/) -ks|_stanford.e
Http://turing.une.edu.au/~cgtools/cgtools97.html

Http://www. infomap.com

Http:// .csi.uottawa.ca/~kavanagh/Ikarus/Cleartalk.html
Http:/Awww.microsoft.com/ntserver/info/indexserver.htm

24


Table of Contents

Acknowledgements
Abstract
Disclaimer

41. Introduction

2. External View
2.1.Partners
2.2. User Interface
2.3. Network

3. Management View

3.1.Application Management
3.1.1. User applications
3.1.2. Research applications

3.2. Data Management

3.3. Knowledge Management
3.3.1. Terms and Concepts
3.3.2. Documents
3.3.3. Knowledge

4. Operational View
4.1. Pilot System
4.2.Menu of Options
4.3. Development Phases
5. Summary

6. References

Appendix A: Conceptual Representation View

Appendix B: CIKS Coating Partnership for Highway Structures

Appendix C: Glossary

=

NOOO

22
26
33

46


Users: DOD

Knowledge base Distribution Channels engineers, coating
specifiers
Expert reasoniag
= > Y

World Wide WEB NT Interactive World Wide
Server Web client

eo :
§ cD-ROM y

Office or field client

Figure B-7: COEX-I system diagram.

COEX-I is a prototype decision-support system that is being distributed for review and
comment. Although the system is based on military structures, parts of the knowledge
base apply to structural steel that is present in highway bridges. Examples include
corrosion failures and videos contained in the system that provide guidance and
inspection procedures for blistering and corrosion causes. Tools used in the
development of COEX-I are being applied to a new system designed to assist Federal
Highway Administration (FHWA) and State Department of Transportation Engineers in
the selection of coating systems for highway steel bridges. Knowledge contained in
the system was developed through various FHWA projects during the past decade.
FHWA and NIST are developing the system jointly under FHWA sponsorship. It will be
operational in late 1997.

43



implement distributed technical databases using ISO, ANSI and de-facto standards for
database storage, query, and retrieval. The use of newer, more flexible de-facto
standards in the form of Microsoft Internet Information Server and the "Access" or "SQL
Server" database management system, provides an opportunity for less costly hardware
and software resources.

WWW Client

ANSI X3.135-1992 Standard:
Structured Query Language

(SQL), Oracle 7 Database
‘Management System

ISO 9579 Standard:
Remote Data Access (RDA)
Interface

CIKS Test Bed Databases

Coating
Product
Database Unix engineering

workstation

Figure B-6: W* database implementation, using an engineering
workstation platform, ISO, and ANS! standards.

B.6 Decision-Support Systems

For the purpose of this article, coating decision-support systems (or expert systems) are
defined as, computerized systems that can contain virtually any type of coating data and
knowledge, such as technical databases, photographs, expert guidance, video and
sound, computer-based models, and a logic module to operate (direct the logical
instruction sequence) on the knowledge and provide an interface to the user.

Many attempts to develop decision-support systems have occurred in technical areas
during the past 15 to 20 years. Examples of decision-support systems developed for
construction materials users can be found in the bibliography at the end of this article.
There are relatively few commercial decision-support systems available today. The
most successful are operational within organizations that are committed to develop,
maintain, and operate them within the organization. Historically, complex systems that
cover a wide-interest area and involve high-level expert knowledge are costly to develop
and maintain. However, advances in decision-support system development tools during
the mid-1980's provided more cost-effective development tools. These tools use the
object-oriented programming [Hend93] architecture and advanced techniques for the
representation and use of knowledge, such as video, sound, and hypertext links. The

4



Data models differ in domain or abstraction.

e Examples of differences in domain include engineering versus science, specialized
versus generalized, and single stage versus multi-stage life cycle.

Modeling restricted to the engineering context is not as complex as when
scientific research is included (e.g., materials engineering versus materials
science).

A specialized model focuses on a certain restricted context such as a particular
type of material and its use. In contrast, a generalized model must be designed
to accommodate multiple types of materials (e.g., coatings, concrete, steel).

A single stage model refers to one stage in the life cycle of a structure. Multi-
stages include more than one life-cycle stage, or the whole life-cycle perspective
(e.g., planning, design, construction, operations, maintenance and demolition).

e Examples of differences in abstraction include conceptual versus physical, data
versus process, and temporal versus non-temporal.

A conceptual model attempts to accurately represent the natural world without
concern for implementation details, while a physical model is an implementation
of the conceptual model using a specific database management system.

Data models are in contrast to process models. If we use the example of a cup
of coffee, the process model would address making the coffee (put x number of
scoops in a filter, fill with water, turn on coffee maker, etc.). The data model
would describe the properties (type of coffee bean, amount of coffee, amount of
water, etc.).

Temporal aspects become important when there is a concern about real-time
issues or the need to maintain a history of all of the transactions to roll back to a
consistent point in time.

These dimensions must be taken into account as models are built to describe an area of
interest and assist in communication among users and applications interoperability. For
example, one might specify whether the context relates to a specialized design-stage
conceptual data model, or a generalized physical life-cycle process model, as well as
the content associated with that model type. One quickly realizes how challenging it is
to create this artificial reality within information models.

Implementation products or tools are available to help manage data and metadata (data
about the data) over the life cycle of planning, design, development, operations, and
maintenance. Implementation products include Computer Aided Software Engineering
(CASE) tools to manage planning, design and development, and Database
Management System (DBMS) tools that deal with operations and maintenance. CASE
tools can be divided into upper or lower CASE tools, where the upper refers to the
planning and design stage, while the lower refers to the application development stage.

The DBMS marketplace includes relational, object-relational, and pure object-oriented
tools where each technology has programming and performance advantages and
disadvantages for different kinds of data. Major database software vendors provide w>
sites to advertise the ease of use and/or performance of their database management
systems for particular applications.

11



"... along-term research and development effort with the ambitious goal of enabling computer
systems to ‘understand’ medical meaning. The project was proposed to Congress as essential to
the development of advanced health information systems -- and as requiring an initial 5-10 year
development phase which would cost $1 to 3 million dollars per year. The Congress responded
with generous and faithful support [W*-3].”
One can find more information by visiting this National Institute of Health project W° site
[W°-3]. David Penniman, Professor and Associate Director of the School of Information
Sciences at the University of Tennessee [StCl96, Penn93] has suggested using a
unified engineering vocabulary, a similar effort along the lines of UMLS for the scientific
and engineering community. There are also international conferences held on the
subject [W°-4] that address the language translation issues in harmonizing terminology
internationally.

There is also relevant work in the ISO community. An example is, the ISO Technical
Committee 37 (TC37), Terminology: Principles and Coordination, which has a Sub-
Committee 3 (SC3) for Computational Aids in Terminology. TC37's SC3 has an ISO
DIS 12200: Terminology Interchange Format (TIF) — An SGML Application, as
mentioned by Alan K. Melby in STP1223 [WS95]. The TIF format “was developed as
part of the Text Encoding Initiative, an international consortium intended to produce
carefully thought out, reusable SGML Document Type Definitions (DTDs) for various
classes of documents and to place these DTDs in the public domain" [WS95]. There is
work from TC37’s SC3, reflected in ISO CD 12620, which addresses exchanging
terminology data between traditional databases, and should be reviewed for the latest
version.

There is existing research in integrating or aligning various terminology or lexical
models from the research community. Specifically, members of an ANSI Ad Hoc
Ontology Standardization Group have investigated integrating different large lexical
models (ontologies) [Hovy97]. This work may indicate that the upper levels of different
ontologies may agree enough to be considered for standardization. Work such as this
is very relevant to the CIKS framework, because the framework's foundation is built on
natural-language analysis of terms and concepts, and assumes that meaning can be
standardized and aligned across different fields to help enable data and knowledge
sharing. (See appendix A for further information.)

3.3.2 Documents

As mentioned previously, a machine-readable document-centric approach for
representing major knowledge sources will be used for the industry. This provides a
stable published knowledge source, which can be referred to from an electronic
implementation of a digital library of machine-readable documents.

One basic premise is that during the document creation or revision phase,
considerations of human comprehension could influence the document structure to
ease automated knowledge acquisition for the future. Efforts from organizations such
as Information Mapping Inc. [W°-15], the University of Ottawa’s ClearTalk [W°-16], and
Formal Object Role Modeling Language [Asym94], as well as research insights from the

13



Acknowledgements

The authors wish to thank Dr. Geoffrey Frohnsdorff, Chief of the Building Materials
Division at NIST, for his long-term support of the CIKS effort. Also we wish to thank Bill
Danner, NIST, and Dr. Robert Amsler, Integrated Resource Group Inc. for many
conversations to discuss some aspect of the framework, and the many reviewers of this
report for their comments.

Abstract

Developing a proposed framework for a Computer Integrated Knowledge System
(CIKS) for construction materials, components and systems requires a practical
approach in order to be utilized by the building materials industry. The need for
practicality has driven the design of the framework to include many commercial
technologies, as well as research results where feasible. This report presents the
framework from the external, management, and operational views, followed by a
conceptual representation discussion in Appendix A. The external view includes the
partners, user interface, and network. The management view addresses the
application, data, and knowledge management. These views are followed by an
operational view, which includes a pilot system, menu of options, and development
phases, which outlines the steps for building an example of the framework. Appendix B
contains a discussion on the CIKS coating partnership for highway structures.
Appendix C , a glossary, concludes the report.

Disclaimer

Trade names and company products are mentioned in the text to adequately
characterize the implementation focus of this framework. In no case does this
identification imply recommendation or endorsement by the National Institute of
Standards and Technology, nor does it imply that the products are necessarily the best
available for the purpose.



CIKS Coating Partnership for Highway Structures

Organization Example
Drivers [functional module] query/results Users

Coatings research, i NIST le.
‘CIKS framework. Failure analysis. por
condition assessment engineers

* cause of failure

Maintenance

(overcoating)
procedures,

FHWA |. + remaining
Coating research, 21 2 service-life
knowledge dissemination Coating system | /S4 + recommended
selection 2 D> procedures
2 + product’material
14 selection
‘Coating maintenance ITYVBIRL |= + terminology &
procedures KS4 ontologies
3
=

Bevelop dina formatsterms SSPC
standards for racic >| Coating Knowledge /—
eater

Figure B-1: CIKS Coating Partnership for Highway Structures

B.2 Examples of Coating Industry Knowledge-Based Systems

Computerized Knowledge-Based Systems (KBS) are available within the coating
industry. Examples of systems developed for construction industry application are
identified in a selected bibliography later in this paper. Many of these systems are
prototypes and were developed when information technologies (e.g., expert system
shells, neural networks) were in their infancy. Features lacking in these systems
include the ability to integrate with the business process and their ability to operate with
computerized systems across different disciplines (e.g., designers, maintenance and
operation staff) and processes (e.g., material and product selection, and cost analysis).

Many KBS are developed for in-house use. Examples include databases on coating
products and services, research data, cost estimating, and decision-support systems.
Many of these systems are rarely distributed to others, in formal or commercial formats.
The reasons include their proprietary nature, lack of an incentive for distribution
(marketability), and costs related to development and maintenance. In fact, many
systems are developed and maintained by personnel responsible for developing the
knowledge and their existence may even go unrecognized by colleagues.

Computerized systems designed for distribution, such as commercial products, require

more effort to develop and maintain. These tend to be more formal systems where
marketing strategies, distribution mechanisms, and customer (user) assistance is

34


6 References

[AFQ89]

[Ams!81]

[Asym94]
[AW97]

[BB89]
[BF81]
[BFRL96]

[BS96]

[DOD95]

[DSS93]
[Eccl97]
[Evens88]

[Fill84]

[Frap96]

[Furu90]

[Giar93]

[GMg3]

[GR90]

J. Andre, R. Furuta and V. Quint (editors), Structured Documents,
Cambridge University Press 1989.

R. A. Amsler, A Taxonomy for English Nouns and Verbs, Proceedings
of the 19" Annual Meeting of the Association for Computational
Linguistics, 1981, pp.133-138.

Asymetrix Corporation, InfoModeler Guide to FORML, Asymetrix
Corporation, 1994.

L. Alschuler and M. Walter, /ntranet vendors shake up the mark at
Documation, Seybold Report on Publishing Systems, v26 n13, April 7,
1997.

B. Boguraev and T. Briscoe (editors), Computational Lexicography for
Natural Language Processing, Longman, 1989.

A. Barr and E.A. Feigenbaum, The Handbook of Artificial Intelligence,
Addison-Wesley Publishing Company, 1981.

Building and Fire Research Laboratory, COEX-/ Expert System for
Steel Protection, Research Update, v2, n3, National Institute of
Standards and Technology, Gaithersburg, MD, Fall 1996.

K. Brady and J. Sullivan, User's Guide for RDA/SQL Validation Tests,
NIST Internal Report 5725, National Institute of Standards and
Technology, Gaithersburg, MD, December 1996.

Department of Defense, Military Handbook: Handbook for Paints and
Protective Coatings for Facilities, MIL-HDBK-1110/1, Naval Facilities
Engineering Command, Lester, PA, January 1995.

R. Davis, H. Shrobe, and P. Szoiovits, What is a Knowledge
Representation, Al Magazine, Spring 1993, pp 17-33.

S. Eccles, Knowledge View tackles Web-based editorial system, The
Seybold Report on Internet Publishing, vi n6, February 1997.

M. W. Evens (editor), Relational Models of the Lexicon, Cambridge
University Press, 1988.

J. Filliben, Dataplot Introduction and Overview, NBS Special
Publication 667, National Institute of Standards and Technology,
Gaithersburg, MD, June 1984.

C. Frappaolo, Creating the optimal document database, Data Based
Advisor, Volume 14, No. 4, pp. 89-93, April 1996.

R. Furuta (editor), EP90 Proceedings of the International Conference
on Electronic Publishing, Document Manipulation & Typography,
Cambridge University Press, 1990.

J.C. Giarratano, CLIPS User’s Guide, NASA, Lyndon B. Johnson
Space Center, Information Systems Directorate, Software Technology
Branch, May 1993.

M. J. C. Gordon and T. F. Melham (editors), Introduction to HOL,
Cambridge University Press, 1993.

C.F. Goldfarb and Y. Rubinsky (Ed.), The SGML Handbook, Clarendon

22


NISTIR 6071

Computer Integrated Knowledge System (CIKS)
for Construction Materials, Components, and

Systems: Proposed Framework

Thomas Y. Kurihara
Lawrence J. Kaetzel

September 1997

Building and Fire Research Laboratory
National Institute of Standards and Technology
Gaithersburg, MD 20899

re,

*%

s

Vg
Sy
A

U.S. Department of Commerce

William M. Daley, Secretary

Technology Administration

Gary R. Bachula, Acting Under Secretary for Technology
National Institute of Standards and Technology
Robert E. Hebner, Acting Director


Appendix C: Glossary

Abbreviation, word
or phrase

AAAI

ASTM

CASE

cD

CIKS

COTS

DAMA
DARPA

Data or datum

DIS

DTD
ERD
FHWA
HAM
HTML
HTTP
IEEE
Inference

Interlingua

Iso
KIF
Knowledge

Knowledge-base
Lexical
Lexicon

NASA
NIAM
NSF
ODA
ODL
ODMA
ODMG

Description

American Association of Artificial Intelligence

American Society of Testing and Materials

Computer Aided Software Engineering

Committee Document

Computer Integrated Knowledge System

Commercial Off The Shelf

Data Administration Management Association

Defense Advanced Research Projects Agency

Something given or admitted; a fact or principle granted; that upon
which an inference or an argument is based (in our loose sense
used to describe what can be stored in a typical commercial
database)

Draft International Standard

Document Type Definition

Entity Relationship Diagram

Federal Highway Administration

Hypertext Abstract Machine

Hypertext Markup Language

Hypertext Transport Protocol

Institute of Electrical and Electronics Engineers

The process of deriving from statements (affirming or denying
something that can be characterized as true or false) either the
strict logical conclusion or one that is to some degree probable;
An artificial language for international communication intended
mainly as a common language for scientists.

International Organization for Standardization

Knowledge Interchange Format

The act or state of knowing; understanding; being intelligent; (in
our loose sense used to include the ability to reason or inference)
A repository of knowledge

Of or pertaining to a lexicon

the vocabulary of a particular language or field arranged
alphabetically

National Aeronautics and Space Administration

Nijssen's Information Analysis Method

National Science Foundation

Office Document Architecture

Object Definition Language

Open Document Management API

Object Database Management Group

46


There were a number of studies integrating relational, semantic, functional, and object-
oriented database technology with document models [AFQ89, Furu90], which at that
time were in the study stage. There are document management systems that use
commercial relational or object-relational databases, but the documents are normally
contained separately. Other higher-end systems handling compound documents, are
more powerful, and are similar to hyperdocuments, which will be discussed below.

There are a number of abstract conceptual models of hypertext systems, including:
Hypertext Abstract Machine (HAM), Trellis, Dexter, Formal Model by B. Lange, and the
Tower model according to De Bra [W°-21]. Some parts of the HAM and Dexter models
have been implemented; otherwise, no other implementation exists for these models.
The Tower Model is an object-oriented, extensible data model for hyperdocuments,
which was presented in 1992 at the ACM Conference on Hypertext. The model
contains basic structural elements, nodes, links, anchors, and objects (tower,
composite, and city). Tower objects are used to model the type, storage, structure,
presentation, and semantic roles. The composites are composites of tower objects,
while the city objects are customized or stylized views of the tower objects. This model
allows objects to exist as a result of a function, allowing them to be virtual objects. In
addition, browsing has been implemented using petri nets.

The W° Consortium is supporting work on a Document Object Model, which is only in
the very beginning stages. As stated on their web site, it is “a platform and language
neutral interface that will allow programs and scripts to dynamically access and update
the content, structure, and style of the documents. The document can be further
processed and the results of that processing can be incorporated back into the
presented page” [W°-22].

A.5 Knowledge Models

From a natural language processing perspective, the study of grammar or syntax is
considered a knowledge representation. The way words are assembled into phrases
and sentences is described by the syntactic approach and the syntax can help define
classes of lexical meaning. However, they are usually “very crude, coarse-grained
taxonomies of meanings in terms of preciously few features” [NR96]. Other researchers
state “there is no way in which meaning can be completely divorced from the structure
that carries it” [NR96].

The Handbook of Artificial Intelligence [BF81] identified knowledge representation
schemes as those including logic, procedural representation, semantic networks,
production systems, direct (analogical) representations, semantic primitives, frames and
scripts. They appear to be divided into two classes: logic, semantic networks, and
direct or analogical representation seem to be fundamental; while procedural,
production, and frame-based representations emerge to be based on one of those
forms with enhancements or modifications.

30


B.5 Technical Databases

Technical databases contain many different types of data (see Figure B-4). Examples
include: product databases that describe material properties and manufacturer data,
laboratory performance measurements, and outdoor exposure test results. It was
stated earlier that significant differences occur among databases due to
designer/developer preferences. These differences take the form of: inconsistent field
names and contents; choice of computer hardware; and software that does not allow
interoperability. The proposed SSPC Guide on coating product data formats is only the
first step in providing compatible databases that can be used among computerized
systems. To realize the full benefit of distributed database exchange, standard methods
must be used to implement and disseminate the data. The steps in developing
interoperable distributed databases include:

establishing consistent database formats and terminology;
establishing a logical schema to represent the physical data in the database;
implementing the database (acquire, computerize) using a database management
system;

¢ providing electronic access through media distribution or electronically, via the
Internet W°.

Decision Support
© coating system selection
failure analysis

Knowledge base.

‘Coatil itabase
suse
+ performance
« service life prediction

poe ce ha nw ep eee eee
| Database files 4
' Accelerated test!
' vo Me parameters
| eating sstem , '
' CONGO r Field exposure test t
L _rideefction parameters 1

1
' 1
| 1
| '
! 1
' i
' '
' '

-cheraceriton

\ Noose
Coin em :

DE.

Figure B-4: Examples of technical coating databases.

Test data .

Significant operational enhancements can be achieved through electronic publication of
databases using the W*. Interfacing database standards such as SQL (Structured
Query Language) and the W® client interface (e.g., WEB browser) reduces the need to
develop multiple user interfaces for different computer platforms and can significantly

39



[w*-1 8] Http://www.oracle.com/products/oracle7/oracle7.3/html/context_opt.html

[W2-19] Http://www.microsoft.com/netmeeting

[W?-20] Http://www.cerc.wvu.edu/WETICE

[w*-21] Http://Awww.win.tue.nl/2L670/static/architecture.html
[w°-22] Http://Awww.w3.org/pub/WWW/MarkUp/DOM
[W°-23] Http://www.w3.org/XML

[W*-24] Http://www.fulcrum.com

[w°-25] Http://www.verity.com

25



Infrastructure for Collaborative Enterprises” [W°-20] and the National Industrial
Information Infrastructure Protocols [W*-8].

Computer-based training has the capability of providing just-in-time training across the
W’ or on CD. This improves human knowledge acquisition and the comprehension
aspect of knowledge management. A recent article [Seacd6] evaluated different
authoring tools including Asymetrix’s ToolBook II [W*-9], which is a W® based product.
Tools such as these allow end users to build their own customized computer based
training products to improve human comprehension, which should be considered within
the context of knowledge sharing and exchange.

The previously mentioned technologies may be augmented by expert systems to
provide additional power or a better user interface. C Language Integrated Production
System (CLIPS) [Giar93] is an expert system tool that is being evaluated for decision
support. Detailed information on CLIPS is available at NASA [W°-10]. CLIPS is of
interest because translators exist between it and knowledge interchange formats.
CLIPS (developed by NASA's Lyndon B. Johnson Space Center) can represent
knowledge as rules, functions, and objects. The National Research Council of Canada
created FuzzyCLIPS [KSLI94], which extends the capabilities of the CLIPS rule-based
expert system shell and allows the representation and manipulation of fuzzy logic
(rules). Fuzzy logic is a function used to express uncertainty, such as conditions that
are neither totally true nor false. FuzzyCLIPS can deal with exact, fuzzy, and combined
logic, allowing fuzzy and normal terms to be freely mixed in the rules and facts, while
using only two basic inexact concepts, fuzziness, and uncertainty. Finally there is the
Java Expert System Shell (JESS) at Sandia National Laboratories [W°-1 1] which is
described as a clone of the core of CLIPS written in Sun’s Java by Ernest Friedman-Hill.

Other technologies, such as intelligent help-desks, could be adapted to maintain best
practices and provide answers to frequently asked questions. A variety of methods may
be used to find similar and relevant past practices. Other commercial solutions are
emerging, which will require further investigation. The goal in this area is to integrate
commercial or commercial-like technology in order to enable a working CIKS framework
for providing user support.

3.1.2 Research applications

Information technology applications that are research oriented include two main areas
of interest for CIKS: software agents and automated knowledge acquisition. Nwana’s
[Nwan96] overview of software agents describes agents that are static or mobile,
deliberative or reactive, autonomous, and cooperative, as well as agents which have the
ability to learn. As agent technology matures and begins to perform human-like tasks,
there will be an ever greater need and benefit for distributed data and knowledge
repositories. Agent technology is where “the rubber meets the road” in terms of using
and testing the benefits of distributed data and knowledge bases. Commercial software
agents (e.g., Autonomy, General Magic, IBM, and Verity) currently exist for this

purpose.



Abbreviation, word
or phrase

O@L

OMT
Ontology
Semantics

SGML

SQL

STEP
TCP/IP
Terminology
Terms

TIF
UMLS
we
Web
XML

Description

Object Query Language

Object Modeling Technique

A model of concepts; relational model of the lexicon;

The study of language meaning; an approach for assigning
meanings to symbols and expressions;

Standard Generalized Markup Language

Structured Query Language

Standard for the Exchange of Product model data

Transmission Control Protocol/Internet Protocol

The terms actually used in any business, art, science, or the like
A word or group of words designating something, especially in a
particular field

Terminology Interchange Format

Unified Medical Language System

World Wide Web

World Wide Web

Extensible Markup Language

47



3.3 Knowledge Management

Initially, a major emphasis of the framework will be on document-based knowledge
management that will be enhanced by emerging technologies with an emphasis on
knowledge sharing or exchange. The justification for this is based upon the fact that
documents currently contain most of the engineering know-how or knowledge in the
form of handbooks, design guides, standards and other natural-language forms. This
section will discuss the areas of terms and concepts, documents, and knowledge, which
comprise the CIKS knowledge management area.

3.3.1 Terms and Concepts

It is theorized that the framework’s foundation should be built on managing diverse
terminology or vocabulary. Precise word meanings are the building blocks for
accurately expressing and specifying technical data and knowledge in every discipline.
To assist in shared communication, it is necessary that word meanings be evaluated on
a common basis. To ensure that the meanings are equivalent for shared
communication requires a rigorous model and methodology to resolve differences in
terms. Identifying different terminology for the same meaning or the same terminology
for different meanings is critical to ensure shared communication.

The Roget’s 21*' Century Thesaurus [Kifp92] uses a Concept Index, which it refers to as
“a semantic hierarchy of the most common concepts we use in American English, as it
is spoken and written today.” This concept index begins with ten concepts: actions,
causes, fields of human activity, life forms, objects, the planet, qualities, senses, states,
and weights and measures. All other concepts fall under one of those ten. The CIKS
framework is evaluating the use of various research communities (e.g., linguistics,
database, knowledge base) to describe the relationships between the concepts that
include and extend beyond a hierarchical structure. Familiar relations include
synonymy, antonymy, and taxonomy while others include part, sequence, cause, and
child relationships [BB89, Evens88, and PB92].

The focus here is to work with industry to engineer the meanings or semantics of the
terminology that will be used to specify all of the data and knowledge within the building
materials industry. We propose building these relational models of the terminology to
lay the foundation for better enabling data and knowledge exchange. There is a need to
identify all of the related dictionaries and thesauruses within each industry, align
differences in definitions, and establish an industry standard dictionary and thesaurus
that would be available on-line. Trade and standards organizations should lead the
effort to provide a unified dictionary and thesaurus, so all future data and knowledge
products can be built on a consistent and engineered foundation of well defined words.

The Unified Medical Language System (UMLS) project, which has been ongoing since

1986, provides some insight into the scale of the problem for the medical community.
The UMLS project was

12