J. Serv. Sci. & Management, 2008, 1: 199-205
Published Online December 2008 in SciRes (www.SciRP.org/journal/jssm)
1
Improving Engineering Data Management with Semantic
Web Techniques
Kai Wang
Mathematics Department, Guizhou University
Received August 27
th
, 2008; revised December 15
th
, 2008; accepted December 22
nd
, 2008.
ABSTRACT
Throughout the IT communities, it has been acknowledged that ontology plays a key role in representation and reuse of
knowledge. This paper discusses some issues of ontology construction for engineering data management, such as
knowledge discovery, knowledge representation and semantic services. All discussions are followed by a running ex-
ample of engineering data management. Based on the user needs with five phases of engineering design, the issue of
knowledge discovery will be presented. The built knowledge base contains basic knowledge models and vocabularies
for knowledge integration. With the semantics of semistructured data, the hierarchical relations of concepts in engi-
neering data have been extracted for reuse in future engineering design based on some clustering techniques of data
mining. Semantic services for engineering design will be provided with the ontology-based schema.
Keywords: engineering data management, semistructured data, ontology, semantic web
1. Introduction
Nowadays, throughout the IT communities, it has been
acknowledged that ontology plays a key role in represen-
tation and reuse of knowledge. However, there is no so
far general methodology for a domain ontology construc-
tion [1]. For reuse and representation of knowledge in
engineering design, one may consider to use ontology as
an unified knowledge model for knowledge representa-
tion and vocabularies.
In this paper, a methodology of ontology-based schema
for engineering data management (EDM) will be dis-
cussed. This paper presents an ontology-based methodology
aiming at reuse and representation of knowledge in engi-
neering design. The built knowledge base will consist of
basic knowledge models and vocabularies. The ontol-
ogy-based schema provides formally defined semantics for
capturing and reusing of design knowledge. With the
machine-interpretable, flexible data structures that can be
modified at run time, ontologies will provide a general
way to knowledge representation and reuse in the EDM
systems. For instance, semantic searching services will be
provided among the heterogeneous engineering databases.
The rest of the paper is organized as follows. In Sec-
tion 2, we present a brief overview of the state of the art
for engineering data management. In Section 3, the user
needs of an engineering data management are presented.
Section 4 discusses the knowledge representation in engi-
neering data management. In Section 5, some facets of
ontology-based services will be discussed. In Section 6,
the conclusion and future works will be discussed.
2. Related Work
2.1. Background
For ontology construction of engineering design, we need
at first to represent a domain ontology in engineering
design. With the integrated knowledgebase, semantic
services will be provided with the ability to detect and
eliminate inconsistencies in heterogeneous sources [2]. In
literature, there are many standards of a construction
project, including the phases in construction [2]. In gen-
eral, these phases can be divided into three parts: plan-
ning; programming; design. In this paper, based on a five
phases of design, we present a domain ontology of engi-
neering design.
EDM (Engineering Document Management) systems
support many facets for maintaining engineering ranging
data from schema design to documentation stage [3]. In
industry practice, EDM systems are typically embedded
in PDM (Product Data Management), PLM (Product
Lifecycle Management), and CAE (Computer Aided En-
gineering). PDM systems provide handing detailed prod-
uct information, ranging from design to production stage.
PLM systems integrate information on CAM systems and
CAD systems, and the information about ERP (Enterprise
Resource Planning) processes. However, traditional
PDM/PLM systems suffer from the inability of reusing of
the creative design knowledge, particular in the concep-
tual design stage. CAE (Computer Aided Engineering)
systems also suffer the same deficiencies as PDM/PLM

200 Kai Wang
systems, lacking of the well-structured product represen-
tation, especially in the early design phases. Like PDM
systems, CAE systems fail to support more complex de-
velopment activities. Thus, reusing of product knowledge
has been hindered by the lack of capabilities to knowledge
representation. Furthermore, the capabilities for direct
process support are also hindered.
In [4,5], the knowledge representation (KR) formalisms
of Quillian’s was proposed as the semantic memory
models. As a knowledge representation method, ontology
represents the relevant domain entities and their relation-
ships by means of classes and relations. Ontologies pro-
vide a unified knowledge model and vocabulary for
knowledge integration in many specific domains. In gen-
eral, ontologies provide machine- interpretable, flexible
data structures that can be changed and adapted at run
time [3,6,7]. In other ways, as XML provides an unified
data exchange schema, the Web ontology language OWL
[8,9,10] is a language for expressing sophisticated class
definitions and properties. In [8,9], Tim Berners-Lee
proposed for the formalization of the resources of web
(no only the web page, but also all the data and resources
on the web). The architecture of semantic web consists of
uniform resource identifier at the lowest layer, XML and
XML schema at upper layer and XML name space, con-
structing the fundamental syntax of semantic web.
2.2. Document Management System
Engineering Document Management systems(EDM) are
important tools for maintaining engineering data [3].
EDM Systems such as Windream [11] or Documentum
[12] are widely used in industrial practice for the storage,
maintenance, and distribution of documents. A step fur-
ther, Product Data Management (PDM) systems provide
extended facilities for the handling of detailed product
information, ranging from design to production stage.
Product Lifecycle Management (PLM) systems is the suc-
cessor of PDM. These systems are widely applied in the
fields of industry manufacturing design. However,
PLM/PDM systems lack essential capabilities to reflect
the function, behavior, and structure for each design
phase. Thus, the management and reuse of design
knowledge are less suited, particular in the conceptual
design stage.
In [13,14,15,16,17,18], the semistructured model was
proposed for the data model and algebra of XML.
As our running example of document management
system, the semistructured data model will be used for
the query and navigating the engineering data, In practice,
the running example offers typical services such as ver-
sion management, change management, notification (if
some changes have been committed), and simple naviga-
tion and retrieval functionality, shown as Figure 1. In our
running example of EDM, the database is modeled in
terms of labeled, directed graph, a semistructured data
model. For improving efficiency, An indexing structure
has also been provided, where the indexing data with its
address are organized in double-level with hierarchy. For
example, when a date data is indexed, the year and month
data at the first level are stored, meanwhile, the date data
is stored at the second level. At running time, the year
and month data will be searched at first level. The second
searching will be completed within the first matching
results. The implementation aspects include some tech-
niques in C++ programming, such as the multiple- user
interface, P2P and components implementation in dis-
tributed environment. As a consequence, irregular engi-
neering data can be accommodated in this model. In the
sequel, the running example offers traditional services,
such like query and navigating the data for five stages
(introduced in the earlier section) in distributed multi-
ple-user environment, meanwhile, a data object is a col-
lection of attributes without blank-value.
3. User Needs
So far, functional modeling research within engineering
design theory studies the flows-the materials, energies,
and signals on which functions operate [19,20,21]. For
functional modeling, domain knowledge conce- ptualiza-
tion is critical to ontology construction.
A general knowledge systems methodology of ontol-
ogy engineering for creating engineering ontology can be
found in literature [22], including four typical steps as
follows:
·Step 1-Determine the ontologies for engineering data
·Step 2-Enumerate important terms and rules in the
ontology
·Step 3-Define the relationship within the ontology,
including hierarchy and the properties of classes
·Step 4-Create instances for engineering data
Analysis of the user needs of engineering data management
is critical to ontology construction for engineering data man-
agement. As an ontology-based methodology, we
Figure 1. A running example of EDM

Improving Engineering Data Management with Semantic Web Techniques 201
should establish our controlled vocabulary (terms) at first.
In the running example of EDM system, there could be
several kinds of ontologies for different purposes, e.g.,
design ontology, documentation (pigeonhole) ontology,
citing ontology, etc. As machine-interpretable, flexible
with data structures ontologies can be modified at run
time.
As the proposed paradigm to knowledge integration for
engineering design, we at first investigate the domain
semantics for engineering design.
There are different phases in the domain of engineer-
ing data management [2]. In general, these phases are
divided into three parts: planning; programming; design.
In this paper, without loss of generality, we consider five
phases in the engineering design, e.g., (1) Feasibility
Analysis, (2) Preliminary Design, (3) Conceptual Design,
(4) Schematic Design, and (5) Documentation, shown in
Figure 3. In each phase, the vocabularies are defined. At
each stage, one need approving services once specific
works completed.
·Step 1-Feasibility Study Phase
In this phase, some methods and techniques are applied
to examine the technical feasibility with the cost evalua-
tion in a project, which proceeds to Conceptual Design
phase once Feasibility Study has been approved.
·Step 2-Conceptual Design Phase
The goal of the conceptual design phase is to identify
very general types of solution. Based on the market re-
quirements and the state-of-the-art of the relevant tech-
nology, the degree of innovation, and the scope for inno-
vation in a design project within a product are determined.
The project proceeds to Schematic Design phase once
Conceptual Design has been approved.
·Step 3-Schematic Design Phase
The goal of the conceptual design phase is to identify
the schematic solution of the project. Schematic Design
establishes a general scope, a conceptual design, scale
and relationships among the components of the project.
The primary objective is achieved with a clearly defini-
tion, feasible concept. A series of rough plans, known as
schematics, should be prepared. In practice, models may
be prepared to help visualizing the project. The project
proceeds to Design Development phase once Schematic
Design has been approved.
·Step 4-Design Development Phase
This phase is to develop more detailed design with the
other aspects of the proposed design. The detailed design
results will served as the achievements of engineering
development. The project proceeds to Documentation
Management phase when Schematic Design has been
approved.
·Step 5-Documentation Phase
Once the Design Development phase had been ap-
proved, the detailed works, as well as the documents in
previous stages should be documented into the library for
future citation and/or reuse.
One could illustrate five phases in the engineering de-
sign as shown in Figure 2, meanwhile, the engineering
document process can be defined as the procedure shown
in Figure 3.
As mentioned in the literature, the user needs of a do-
main ontology are served as the skeleton of a domain
ontology. Based on the user needs of a domain ontology,
the syntactic elements of an ontology, such as properties,
vocabularies and their relationships, are obtained. Ab-
stract concepts can be formed with some clustering tech-
niques, such as formal concept analysis (FCA). Rules
with respect to properties and vocabularies can also be
formed primitively. In the sequel, knowledge about re-
fined concepts and their relationship is learned statically
or dynamically with earlier mentioned models or an iter-
ated process.
Feasibility
Study Conceptual
Design
Schematic
Design
Design
Development
Pigeonhole
Management
Figure 2. Overview of engineering design process
Figure 3. Overview of documentation process

202 Kai Wang
4. Knowledge Discovery in EDM
In literature, knowledge discovery is the process of find-
ing novel, interesting, and useful patterns in data. Domain
conceptualization aims at identifying and defining con-
cepts, specifying relationships among the concepts. Cap-
turing domain concepts and their relationships is a bot-
tleneck for ontology engineer [1]. One could capture
concepts and their relations automatically by some clus-
tering techniques, such as formal concept analysis (FCA).
Then knowledge discovery and semantic services for
engineering design will be provided.
For knowledge reuse and semantic services in engi-
neering data management, knowledge discovery is crucial
for a knowledge management system. However, in in-
dustrial practice, people are reluctant to share knowledge
by making their knowledge explicit. One of the reasons
for this is that it requires extra effort and they seldom
hardly benefit from the explicit knowledge. Meta infor-
mation about products is often chosen inconsistently and,
similarly, work processes and decision making proce-
dures are captured by different users in different ways.
Consequently, knowledge acquisition is severely limited
and could be found by some query. A solution to these
problems is to automatic knowledge acquisition, called
automatic knowledge discovery. The current ontology
learning systems extracts relevant domain terms and rela-
tionships from a corpus of text. As an unsupervised
learning method, formal concept analysis is a good tool
to extract concepts and implied rules. With formal con-
cept analysis (FCA), one can extract formal concepts and
their relationships fro a boolean context. The basic rela-
tion of the concepts is the hierarchical relation, which is
the core component of all ontologies.
Formal concept analysis (FCA) is a clustering tech-
nique which concerns about the hierarchical grouped
structure of objects, which is a good start point for con-
cept construction [23]. FCA reflects the philosophical
understanding of a concept as a unit of thought consisting
in two parts: the extent containing all the entities belong-
ing to the concept and the intent which is the collection of
all the attributes shared by the entities, based upon Galois
connections [2,23,24]. A boolean context
C
is defined
as a triple
( )
G MI
, ,
, where
G
is the set of objects or
entities and
M
the set of properties or attributes. The
formal concepts set
F
can be derived from this context
as follows. For
A G
⊆
and
B M
⊆ ,
let us define the
Galois connections:
{() }
AmMgA gIm
′
=∈| ∀∈,
{() }
BgGmB gIm
′
=∈| ∀∈.
Thus
'
A
is the set of the attributes that are common to
all the entities in
A
and
'
B
is the set of the entities
possessing their attributes in
B
.
These Galois connec-
tions verify all the usual properties of the duality in par-
ticular the following relation
A A
′′
⊆ stands for any
subset of the context (consequently
)
A A
′ ′′′
= . Concept is
then a pair
( )
A B
,
such that
AG BM
⊆ ,⊆,
B A
′
=
,
and
A B
′
=
.
A
is the extent of the concept and
B
the
intent. For concepts
1 1
( )
A B
,
and
2 2
( )
A B
, ,
the hierar-
chical subconcept superconcept relation is formalized by
1 1221221
() ()()
A BABAABB
,≤,⇔ ⊆⇔⊆.
The set of all
the concepts of the context
( )
G MI
, ,
together with this
order relation is a complete lattice which is called the
concept lattice Therefore for every set of concepts there
exists a unique largest subconcept, the infimum and a
unique smallest superconcept, the supremum. Supremum
and infimum are respectively given by
( )
j Jjj
A B
∈
∨, =
(( ))
jJ jjJj
A B
∈ ∈
′′
∪ ,∩ and () (
j Jjjj Jj
A BA
∈ ∈
∧,=∩,
() )
j Jj
B
∈
′′
∪ .
In semistructured data model, such as Lorel and XML,
the data model is a multi-labeled graph, where the data
objects are nodes and the attributes, labels [2]. In our
running example, a data object is a collection of attributes
without blank-value. Thus, it is very easy to identify
whether a data object has an attribute. By conceptual
scaling, an engineering document can be transformed into
a boolean context.
With standard FCA algorithms, the hierarchical
grouped structure of engineering data objects, called
concept lattice, can be formed. Then the collection of
implications (association rules) will be discovered. This
will provide useful and applicable knowledge integration
for reusing the engineering data.
5. Knowledge Representation
In philosophy, ontology is a theory about the nature of
existence. A formal definition of ontology used in this
paper bases on [25]: “An ontology is a tuple
( )
C R
ψ
Ω =,,,
whereas
C
is a set of concepts,
R
a
set of relationship names,
H
R "is_a
:= "
R
∈
the in-
heritance relation on
C
,
A
R R
∈
the attribute relation
on
C
and
( )
RC C
ψ
:→℘×
a function”. In short, an
ontology is the conceptualization of concepts and their
relations.
There are many kinds of representation of ontology.
For instance, a typical ontology may include the concep-
tual entity of the domain, called concept, attributes de-
scribing a concept, called property, relationship between
concepts, called relation, relation via logic expression,
called axiom. Ontology represents the interrelationships
between entities or resources, while XML and RDF deal
with metadata. An ontology is typically defined as a
specification of conceptions, where there are definitions
of entities and their relationship with each other, based on
semantic nets used in artificial intelligence.

Improving Engineering Data Management with Semantic Web Techniques 203
One can discover a domain knowledge through super-
vised or unsupervised methods. As mentioned earlier,
FCA is an unsupervised method to forming abstract con-
cepts and rules. In issues of ontology learning, one can
construct an ontology for engineering design automati-
cally. However, the resulted ontologies are often unsatis-
fiable without human intervention. In this paper, we pre-
sent an example of knowledge representation for our run-
ning example of EDM, shown in Figure 4. In the engi-
neering knowledge domain, there are at least two kinds of
entities, persons and documents. The former includes the
attributes as name, email and can be divided into deviser,
document managers and auditors, etc; the latter includes
various attributes of documents, such as visiting time,
documentation time, file location, data size, data type, etc.
The relationships include hierarchical attributes, etc.
Then we attempt to represent the knowledge in our
running example of EDM as axioms of
DLs
.
DLs
are
the historical descendants of attempts to formalize se-
mantic networks, which can be addressed by utilizing a
subset of first order logic [23]. A
DL
-based system can
support modeling of rules for engineering data in a hier-
archical fashion. A typical
DLs
knowledge bases con-
sists of a TBox calT, an ABox ∑ (a set of assertions), and
a set of rules Ř. The set of rules Ř is of the form
C D
⇒
,
where
C D
,
are concepts. Given an individual
a
(a
documented document or person), a new assertion
( )
D a
will be added into the ABox ∑ if
( )
C a
is already be-
lieved to hold. The lower expressivity of description lo-
gics, e.g.,
AL N
ε
, limits the ability to define detailed
semantics for a domain. However, in return they make
several positive tradeoffs, including desirable comput-
ability and tractability results [23]. Within the repository
application, this loss of expressiveness is acceptable so
long as enough design knowledge can be captured to en-
able necessary operations, such as matching devices
against adequately sophisticated searches and comparing
mechanisms.
We denote by the set of atomic concepts in ∑,
T
Symbols
[23]. Now let us demonstrate some axiomatic
representation with respect to the model shown in Figure
4.
In graph-based presentation of ontology, the atomic
concept is often presented as a class. In the ontology of
engineering design below, the ABox ∑ consists of the
assertions with respect to the documented documents and
persons, and
{ ,
}
T
SymbolsPerson Document_Manager
DesignerAuditor Document
= ,
, ,
(1)
Besides the inheritance relations, the properties (slots) of
concepts could also be described as roles. Thus, the set of
atomic roles contains
{
(2)
(3)
} (4)
hasName hasEmaildesgin
audit document designByauditBy
documentBy cite hasLocation
hasType hasSize hasDocumentationtime
,, ,
,,, ,
, ,,
, ,
T consists of the following axioms
Person
hasName
∃ .
hasEmail
∃ .
(5)
Designer Person
≡
desgin Document
∃ . (6)
Auditor Person
≡
audit Document
∃ . (7)
Document_Manager Person
≡
document Document
∃ .
(8)
Document
isCited
∃ .
hasLocation
∃ .
hasType
∃ .
hasSize
∃ .
(9)
hasDocumentationtime
∃ .
(10)
We provide some rules for the ontology above, where
the rules set Ř contains
desgin DocumentDesigner
∃ .⇒
audit DocumentAuditor
∃ .⇒ (11)
document DocumentDocument_Manager
∃ .⇒ (12)
cite DocumentisCitedDocument
∃ .⇒∃. (13)
6. Semantic Search Service
After the preliminary ontology construction for engineering
data, further steps, e.g., integrating all concepts and rela-
tions in distributed environment and evaluating the on-
Figure 4. An ontology example of engineering design

204 Kai Wang
tology, should be investigated. As mentioned earlier, the
ontology-based semantic services should include con-
structing domain concepts and their relationships. Fur-
thermore, a sophisticated search and comparing mecha-
nism, which are advanced semantics services, should also
be provided. One can demonstrate some possible
DL
-
based semantics services as follows:
1) Asserting new information about an existing term
(individual)
2) Recognizing that the updated term is an instance of
a class (concept name)
3) Firing a rule on the term that is associated with the
class
4) Propagating information from the updated term to
related terms
One can offer inference service to reason with
DLs
-based knowledge. For example, given an individual
a
(a documented document or a person), one can find
the most specific concepts for
a
e.g., the most proper
description of
a
within the domain. One can also pro-
vide other kinds of intelligent services, such knowledge
discovery, semantics searching, etc [19].
In
DLs
, given an ontology
∑
, a query
φ
could be
written in the form of
( )
C a
?
, where
a
is an individual
and
C
, a concept description. It could also be expressed
as an evaluation of the consequence relation
|
φ
∑=
,
with the only two answers to that query: either ‘yes’, or
‘no’. For example, in the ontology above, the problem
whether the
Designer
are
Auditor
are same for the
document
Doc
can be expressed as the entailment prob-
lem:
∑
|= (
designBy Designer
∃ .
)( )
Auditor Doc
(14)
7. Conclusions and Future Works
As mentioned above, we discuss some issues of ontol-
ogy-based schema for engineering knowledge systems.
Based on the user needs of engineering data management
systems, we discuss the issues of knowledge discovery
method with formal concept analysis. The knowledge
representation of engineering data management systems has
also been discussed. Furthermore, we have investigated
the domain semantics. The proposed works could be
served as major steps for constructing an engineering
data knowledge management system.
However, many problems are still open. For example,
it is still challenging to meet the needs of providing far
more semantics services for creative design processes in
engineering data management. In all, creative engineer-
ing data management is attractive but challenging topic.
As knowledge discovery is currently the major bottleneck
of knowledge-based system, one needs more effective
and efficient methods to extract relevant domain terms
automatically. Appropriate concepts can meet the re-
quirements of engineering data knowledge management
system and describe the domain knowledge properly.
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