Journal of Service Science and Management, 2012, 5, 339-347
http://dx.doi.org/10.4236/jssm.2012.54040 Published Online December 2012 (http://www.SciRP.org/journal/jssm)
Personalized Multimedia Integration for the
Heterogeneous Museum Systems Using the Ontology
Mapping Approach
Ngamnij Arch-int, Chatkamon Anontachai
Semantic Mining Information Integration Laboratory (SMIL), Computer Science Department, Science Faculty, Khan Kaen University,
Khan Kaen, Thailand.
Received October 3rd, 2012; revised November 12th, 2012; accepted November 22nd, 2012
ABSTRACT
Presently, many museums have developed their own multimedia information systems to store the artifacts and other
objects of scientific, artistic, cultural or historical interest into the digital resources and make them available for public
viewing on the Web. However, searching for the multimedia information is still not relevant to the user requirement,
and the system does not provide meaningful information. This research work proposes the personalized multimedia
integration system for museums based on ontology which is a core component of the Semantic Web technology. The
multimedia information for each resource has been expressed in the Web Ontology Language (OWL). The research also
resolved the problem of information integration by proposing the ontology mapping technique to cope with the seman-
tic conflicts and structural conflicts via the OWL properties. Then the ontology storing users’ interest was designed
which matched the museum’s ontology so that retrieval of multimedia information is meaningful and direct to the users’
needs.
Keywords: Ontology Mapping; Personalized Multimedia Integration; Heterogeneous Museum Systems; Semantic Web
1. Introduction
Nowadays, many museums have developed their own
multimedia information systems to store the artifacts and
other objects of scientific, artistic, cultural or historical
interest into the digital resources and make them avail-
able for public viewing on the Web. However, each mul-
timedia information system can be developed or procure
independently that differs from place to place. As a result,
the systems as a whole have become heterogeneous and
dependent on a variety of applications or database man-
agement systems. This heterogeneity has led to the fol-
lowing problems: 1) Each museum has its own informa-
tion storing format or structure. For instance, Museum 1
names its object information table as “Object”, whereas
Museum 2 names the table as “Resource”. Museum 1
stores creator’s or object inventor’s information in a
creator table. Museum 2 stores its creator’s information
as an attribute in another table, which may make it diffi-
cult to find relationships between information sources;
)2 Retrieval of information from each source is on the
most part of keyword-based. The system still cannot re-
trieve information meaningfully by applying words that
have similar meaning; 3) The system cannot directly re-
trieve what the user needs if the user does not specify his
or her requirement each time he is searching. For exam-
ple, a user who prefers a video file should specify every
time that he wants the video file.
In this paper, we present the ontology-based personal-
ized multimedia integration architecture of a museum
system which is a major component of the Semantic Web
Technology [1]. The multimedia information structure
from each source is extracted into the ontology expressed
in Web Ontology Language (OWL) [2]. The local ontol-
ogy from each source is integrated through the ontology
mapping process to form the ontology-based personal-
ized multimedia domain. This research aims to resolve
the semantic and structural conflicts occurred during the
ontology mapping process. To resolve the semantic con-
flicts, the research employs the principle of semantic
similarity measurement through the WordNet [3] data-
base. The research also employs the OWL properties to
resolve the semantic conflicts, as well as the structural
conflicts.
In addition, to obtain the results in accordance with the
user’s interest and requirement, the research transforms
the personalized user profile into an ontological model.

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach
340
The user information stored in the user profile ontology
can be used as a criterion to query the museum’s infor-
mation ontology so as to achieve efficient information
retrieval that is mostly direct to the user’s interests.
2. Literature Reviews
2.1. Semantic Web Technology
The Semantic Web technology enhances the capability of
the present day Web technology to enable computer or
software agent to understand Web information (machine
understandable) which corresponds to human’s under-
standing. Then the information can be further processed
and managed by computer efficiently. Development of
Semantic Web employs the ontology as an important
component. The ontology defines a common vocabulary
explicitly without any ambiguity so that both human and
computer or software agent can understand and share
information in a domain. One may consider to use on-
tology as an unified knowledge model for knowledge
representation and vocabularies [4]. Hence, the semantic
conflicts of information can be solved and the computer
or software agent is able to search for the synonymous
terms with similar meaning. Generally, ontology consists
of classes or concepts, which in turn comprise groups of
things called instances which have the same properties.
Classes and instances can create relationships between
classes or between instances. The relationship between
classes can be called the subsumption hierarchy, whereby
a general class (or superclass) subsumes more specific
classes (or subclasses). The subsumption hierarchy is
used to store properties at the level of generality and
automatically provide them to the lower level of specific
concepts through the inheritance mechanism. In addition,
there is a general relationship that relies on properties as
the connector. The property that links relationships be-
tween classes or between instances are called the Ob-
jectProperty, whereas the property that connects classes
or instances with literal is called the Data Type Property,
which is the property used to describe each instance or
class characteristic. Creating machine understandable
ontology for computer or software agent requires trans-
formation of ontology structure into a language form
such as the Resource Description Framework (RDF/RDF
Schema) [5,6] and Web Ontology Language (OWL).
OWL has RDF/S as its sublanguage, but adding more
advanced constructs to describe semantics of RDF that
enables the computer to understand the information
meaning more than RDF/S. Moreover, in information
search and retrieval from ontology, many more lan-
guages have been developed, for example, RQL [7],
RDQL [8], SPARQL [9], OWL-QL [10], etc. This research
relied on OWL to express the ontology structure and
used SPARQL for searching and retrieving information
from the ontology.
2.2. DCMI—Dublin Core Metadata Initiative
[11]
The DC is the specialized metadata vocabulary defined
by the Dublin Core Metadata Initiative (DCMI). The
DCMI is an organization dedicated to promoting the
widespread adoption of interoperable metadata standards
for describing a wide range of networked resources. The
DC consists of a set of predefined properties for describ-
ing digital resources unambiguously. The DC standard
encompasses two levels: Simple and Qualified. Simple DC
[12] (see Figure 1) comprises fifteen standard elements,
for example: title, creator, subject, description, publisher,
and so on; whereas Qualified DC [13] employs additional
qualifiers called “Dublin Core Qualifiers” (DCQ) to fur-
ther refine the meaning of a resource, for example: an
element abstract is defined as an alternative qualifier to
refine the description element. The simple DC usually uses
prefix dc as “http://purl.org/dc/elements/1.1/” namespace to
annotate each standard element, whereas qualified DC usu-
ally uses prefix dcterms as “http://purl.org/dc/terms/”
namespace. For example, the dc:title is used as a name
given to the resource. Use of Dublin Core metadata en-
ables resource owner to define explicit resource terms
without ambiguity and lessen the problem of sharing and
exchanging resource data between systems. A complete
description for the DC metadata can be found in [11].
2.3. Related Works
Many research studies attempted to solve the problem of
information integration from heterogeneous data sources.
A number of studies [14-17] aimed at solving problems
in multiplicity of information structures both in the data-
bases and Web bases, by using ontology as an assistant
mechanism for representing the information structure and
mapping information from different systems which have
dc:ri
g
hts
dc:source
dc:lan
g
ua
g
e
dc:identifie
r
dc:descri
p
tio
n
dc:t
yp
e
dc:forma
t
dc:covera
g
e
dc:date
dc:relatio
n
dc:contributor
dc:sub
j
ect
dc:title
dc:creato
r
dc:
p
ublisher
Figure 1. The simple DC standard [11].

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach 341
structures and names discrepancies. This makes the in-
formation meaning understandable, and relationships
between the information are correlated. The integration
of information from multiple sources has to cope with the
problems both in terms of structure and semantic con-
flicts. A lot of research works, for example [18-21] tried
to solve the problem via ontology mapping [22] by ap-
plying various tools developed for the mapping or by
relying on the WordNet and OWL properties to solve the
problems. In the context of ontology personalization,
D.-N. Chen, and Y.-C. Chiang [23] integrated ontology
and collaborative filtering to design a system to provide
information recommendation service. The system col-
lects the information of the users and could learn the pre-
ferences of every user and those preferences in common
which could be recommended to the users. X. Aimé, F.
Furst, P. Kuntz and F. Trichet [24] provided the similar-
ity measure dedicated to the personalization of a Domain
Ontology by mainly adapting the content of an ontology
to its context of use. An approach aims at talking about
several parameters such as culture, educational back-
ground and emotional state to reflect the relevance users
of ontologies perceive on the subclass hierarchies and to
what extent the terms associated to the concepts are rep-
resentative. Some other research studies [25,26] empha-
sized on solving the integration of museum information
from heterogeneous sources. However, most research
works have not specifically solved structural conflicts
and information retrieval does not really convey meaning
directly according to the individual’s interest.
3. Research Methodology
3.1. System Architecture Design
This research designed the system architecture consisting
of three layers as shown in Figure 2, i.e., Resource Layer,
Mediator Layer and Presentation Layer. Each layer has
been designed with the following details:
3.1.1. Resourc e La yer
The resource layer is the layer in which each museum
stores its multimedia data. This layer consists of the fol-
lowing components:
1) Database—This refers to the museum database for
storing the multimedia digital resources and the person-
alized user profile.
2) Wrapper—The transforming of information stored
in the relational database to the ontology expressed in
OWL.
3) Local Ontology—Result of information transforma-
tion from the Wrapper module. The local ontology ex-
traction consists of the personalized user profile ontology
for storing users’ interests and museum ontology for
storing the multimedia information. The extracted local
Figure 2. The system architecture design.
ontologies are expressed in OWL which enables machine-
understandable and semantic retrieval.
3.1.2. Mediat o r Layer
This layer mediates the information retrieval and integra-
tion system. The mediator layer is composed of the fol-
lowing operating components:
1) Semantic Personalized Search receives command
from the user interface in the Presentation Layer and re-
trieves information from the mediator ontology accord-
ing to user’s condition.
2) Ontology-Based Personalized Multimedia Domain
is a system’s mediator ontology built from mapping of
the museum local ontologies. This ontology is used as
the main component for retrieving media information of
the museum accurate to user’s interest.
3) Semantic Museum Ontology Mapping is the module
for mapping of the local museum ontologies from multi-
ple museum resources. The semantic conflicts and struc-
tural conflicts arise during the ontology mapping process.
To solve the semantic conflicts, this module employs
Wordnet database to calculate the similarity value be-

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach
342
tween the concepts pair and the properties pair. The rela-
tion is built through OWL properties such as owl:equi-
valueClass and owl:equivalentProperty. To cope with the
structural conflict, the solution relies on other properties
of OWL such as owl:Restriction, owl:onProperty, and
owl:someValuesFrom in bridging the different ontologies
with structural conflict. The outcome of ontology conflict
solution leads to the Ontology-Based Personalized Mul-
timedia Domain.
4) Word Net database assists in locating semantic
similarity of classes or properties between local ontolo-
gies which will be integrated to build a mediator ontol-
ogy of the system.
5) OWL Reasoner is a tool providing various reason-
ing services for OWL documents, such as OWL species,
consistency checking, satisfiability, and entailment test.
The research employs the Pellet reasoner [27] for seman-
tic information retrieval of the system’s mediator ontol-
ogy. The reasoner enables the computer to retrieve
meaningful information and infer new knowledge stored
in the mediator ontology to obtain deep relationship in-
formation not visible to general users.
3.1.3. Presentation Layer
This layer provides the user interface to receive registra-
tion data of users and record in the personalized profile
database. The layer also receives retrieval command and
conditions from the user, and then exhibits results of in-
formation retrieval from ontology and arrange appropri-
ate format for the user.
3.2. Ontology Design
The museum information resources for research experi-
ment were derived from two museum systems with dif-
ferent database structures. The information from each
system’s database was extracted into ontology structure
via the wrapper module. This research applied the re-
search work [28] and the tools given in [29] for extract-
ing information from database structure into ontology
structure expressed in OWL. For this section, example of
ontologies built from two sources of museum and a user
profile ontology built from the personalized profile data-
base will be shown.
The museum ontology of Museum 1 (see Figure 3) is
the ontology extracted from the database schema of the
Museum 1’s system. The ontology structure is depicted
as a graph consisting of classes, relationships between
classes in the form of subsumption hierarchy, and the
properties called the ObjectProperty such as status, pe-
riod, and collection. These properties have been designed
to have an object class as a domain, and the superclass
properties can be inherited to different child classes.
1) The museum ontology of Museum 2 (see Figure 4)
is the ontology extracted from the database schema of the
Museum 2’s system. This ontology employs the Dublin
Core Metadata Element Set-DCMES to describe infor-
mation, such as dc:title to describe the resource title,
dc:format to describe the resource format and dc:lan-
guage to describe the resource language.
2) The museum ontology of Museum 2 (see Figure 4)
is the ontology extracted from the database schema of the
Museum 2’s system. This ontology employs the Dublin
Core Metadata Element Set-DCMES to describe infor-
mation, such as dc:title to describe the resource title,
dc:format to describe the resource format and dc:lan-
guage to describe the resource language.
3) User Profile Ontology, as shown in Figure 5, stores
personal information and information about interest of
users. This includes period, material, category, and for
Figure 3. Ontology structure for storing data of Museum 1.

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach 343
Su bClaassO
f
Su bClaassO
f
Su bClaassO
f
Su bClaassO
f
Figure 4. Ontology structure for storing data of Museum 2.
Figure 5. The user profile ontology.
mat of the objects which are stored into the user interest
classes. The museum ontology classes associated to the
user interest classes are combined through the owl:union
Of property, such that the instances retrieved from the
museum ontology classes are shown for a user to specify
his/her interest.

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach
344
3.3. Ontology Integration
Integration of data from ontologies that is derived from
heterogeneous sources requires consideration of the het-
erogeneity problems. For this paper, we classified the
heterogeneity problems into two main levels: the seman-
tic heterogeneity and the structural heterogeneity as fol-
lows.
3.3.1. Semanti c Heterogeneity
Occurs when there is a disagreement on the meaning,
interpretation, or intended use of the same or related data.
The semantic heterogeneity can be classified into various
types, such as, semantic conflicts, property value con-
flicts, scaling conflicts, and so on. This paper focuses on
semantic conflicts as described below:
Semantic conflicts, are concerned with the semanti-
cally equivalent classes or properties defined by different
names. To solve the semantic conflicts, the concepts
(classes or properties) pair from different ontologies is
compared and computed the semantic similarity value
based on the similarity value equation. In this research,
the equation proposed by Wu and Palmer (wup) [30] was
selected because it was designed on the basis of the
WordNet to measure semantic similarity. The semantic
similarity assessment is achieved by terming the com-
pared words “concepts” as in the following Equation (1).
 

 
12
12
12
2,
,
wup
depth lcscc
Simc cdepth cdepth c

 (1)
where depth is the distance from the concept node to the
root of the hierarchy, and lcs(c1,c2) is the lowest common
subsumer of c1 and c2.
The similarity score is 0 < Simwup(c1, c
2) ≤ 1 and is
never zero since the depth of the lcs is not 0. And if Sim-
wup(c1, c2) = 1, then concepts c1 and c2 are in the same
synset, i.e., similar meanings (c1  c2) although different
words are used.
When the similar pair of concepts was obtained, the
OWL property was applied to solve the semantic conflict,
for example, owl:equivalentClass or owl:equivalent-
Property, as shown in Table 1 and Table 2, respectively.
3.3.2. Str uc tural Heterogeneity
Occurs when the same concepts of different systems are
modeled with different logical structures. The principle
of structural conflict consideration was based on the
characteristics of data storing. Although, there are vari-
ous types of structural heterogeneity, this paper focuses
on schematic discrepancies as described below:
 Schematic discrepancies occur when the logical stru-
cture of a set of properties and their values belonging
to a concept in one system are organized to form a
different structure in another system. For example,
Ontology 1 might store or differentiate data in a sin-
gle class, while Ontology 2 might store the same type
of data in Ontology 1 through the property, as shown
in the example in Figure 6. It can be seen that the O1:
Photograph class is equivalent to the concept O2:
Resource whose property dc:type has the range as the
concept O2: Image.
 In most cases no direct concept to concept mapping is
possible. Solution of structural conflicts in this re-
search was achieved through the use of OWL proper-
ties, namely, owl:equivalentClass, owl:onProperty,
and owl:someValueFrom, as shown in Figure 7.
Table 1. Result of calculation of similarity score between
selected classes.
Ontology 1Ontology 2 Similarity
value Similarity structure
Object Resource 0.625 owl:equivalentClass
Provenance Provenance
Statement 1 owl:equivalentClass
Location Location 1 owl:equivalentClass
Period PeriodOfTime1 owl:equivalentClass
Period Period 1 owl:equivalentClass
Category SubjectScheme0.75 owl:equivalentClass
Table 2. Result of calculation of similarity score between
selected properties.
Ontology 1Ontology 2 Similarity
value Similarity structure
Name dc:title 0.9333 owl:equivalent
Property
Description dc:description 1 owl:equivalent
Property
Format dc:format 1 owl:equivalent
Property
Added_Date date 1 owl:equivalent
Property
Category dc:subject 0.75 owl:equivalent
Property
Provenance Provenance 1 owl:equivalent
Property
Figure 6. Example of schematic discre panc ies.

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach 345
Figure 7. Use of OWL properties to solve the structural
conflicts.
3.4. Research Experiment
The Ontology-Based Personalized Multimedia Domain
derived from ontology mapping of different sources can
be used as a core component for semantic data retrieval
through the SPARQL as in the following examples:
Example 1 illustrates a query of data from ontology
with structural conflicts (as shown in Figure 6). The
query in Figure 8 shows a request for the photograph
files in the ontology or all instances of the Photograph
class.
The results executed from SPARQL command in Fig-
ure 8 return all instances of the Photograph class of On-
tology 1 and instances from the Resource class in Ontol-
ogy 2 which has the dc:type values as instances in the
Image class. A portion of query results is illustrated in
Figure 9.
It can be seen that when the semantic property of
OWL is used in the construction and integration of on-
tologies, semantic retrieval is more efficient. User can
use terms defined in one ontology to locate information
from one source, but the system is able to retrieve more
results from another ontology.
Example 2 illustrates a query of data from ontology
according to the users’ interest. For this example, Table
3 illustrates the user’s interest stored in the user profile
ontology (Figure 5) and Table 4 shows details of object
files existing in Ontology 1 and Ontology 2.
The query in Figure 10 shows a request for the object
files in the ontology that have format corresponding to
the users’ interest.
With the benefits of OWL properties in enabling great-
er inferencing, the museum ontology 1 can define the O1:
format property to be inversed of the O1: formatOf via
the owl:inverse Of property, as shown in Figure 11.
Once the O1:formatOf property is defined to be in-
verseOf the O1: format property, user can pose a query
by using either O1:formatOf or O1 format property
without creating the instance statement for the O1:for-
matOf property.
Figure 8. SPARQL command for retrieving the photograph
files.
Figure 9. Instances results retrieved from the photograph
class.
Figure 10. SPARQL command for retrieving the object files
corresponding to the user’s interest.
Figure 11. Using owl:inverseOf property.
Table 3. Example of the user’s interest stored in the user
profile ontology.
User:user_001
Class Instance
InterestPeriod Renaissance
InterestMaterial gold
InterestCategory art
InterestFormat pdf
Table 4. Example of object file description stored in the
museum ontology.
Object Period Material Category Format
O1:art_001Rebirth gold art pdf
O1:art_002Rebirth wood video avi
O2:art_003 Renaissance gold art pdf

Personalized Multimedia Integration for the Heterogeneous Museum Systems Using the Ontology Mapping Approach
346
The results executed from SPARQL command in Fig-
ure 10 return all instances of the Object class of Ontol-
ogy 1 and instances from the Resource class in Ontology
2 which was found in the Renaissance period, made from
gold, classified in the art category, and has a pdf format.
Hence, the object O2:art_003 and O1:art_001 instances
that are most matched with the user’s interest are re-
turned to a user view.
3.5. System Evaluation
This research evaluated the results of experiment and
assessed the efficiency of information retrieval from in-
tegrated ontologies based on the Precision, Recall and
F-Measure [31] values. Precision is the ratio of the num-
ber of relevant records retrieved to the total number of
irrelevant and relevant records retrieved. Recall is the
ratio of the number of relevant records retrieved to the
total number of relevant records in the ontology. Preci-
sion and Recall values are calculated from Equations
)2( and (3) as follows:
Precision 100%
A
A
B

 (2)
Recall 100%
A
AC

 (3)
where A is number of relevant records retrieved. B is
number of relevant records not retrieved. C is number of
irrelevant records retrieved.
Precision and Recall values can be used to calculate
F-measure which is defined as a harmonic mean of pre-
cision and recall, as shown in Equation (4) below:
Precision Recall
2Precision Recall F







(4)
Evaluation of the retrieving efficiency of information
interested by 30 users shows that most objects found
corresponded to the interest of users. The object informa-
tion is retrieved on the criterion of classification, format,
material, and period of each object with the Precision
value of 1.00 the Recall value of 0.93, and the average of
overall efficiency or F-measure was 0.96. These results
were in very high levels.
4. Conclusions
This research designed and developed the integration of
multimedia information interested by individuals in the
museum system based on Semantic Web technology. The
multimedia information sources were derived from two
museum systems and extraced into ontologies. The re-
searchers developed ontologies with OWL language and
integrated the ontologies based on the ontology mapping
technique. To solve the semantic heterogeneity, the prin-
ciple of semantic similarity values via the WordNet is
applied to the research. To solve the structural heteroge-
neity, we employed the OWL properties to be used dur-
ing the ontology mapping process. In semantic data re-
trieval, SPARQL language was imperatived for retriev-
ing data in ontologies.
The experiment shows that data retrieving according to
30 users’ multiple interests corresponded to their re-
quirements. This was based on the information of each
object’s period, material, classification, and format, with
a Precision value of 1.00, Recall value of 0.93, and an
average F-measure of 0.96, indicating high levels of ex-
perimental results.
5. Acknowledgements
We deeply thank to Department of Computer Science,
Faculty of Science, Khon Kaen University for financial
support of the research due to research grant: CSK-
KU12/2554#8.
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