J. Service Science & Management, 2009, 2: 322-328
doi:10.4236/jssm.2009.24038 Published Online December 2009 (www.SciRP.org/journal/jssm)
Combining Personal Ontology and Collaborative
Filtering to Design a Document Recommendation
System
Deng-Neng CHEN, Yao-Chun CHIANG
Department of Management Information Systems, National Pingtung University of Science and Technology, Taiwan, China.
Received July 31, 2009; revised September 15, 2009; accepted October 24, 2009.
ABSTRACT
With the advance of information technology, people could retrieve and manage their information more easily. However,
the information users are still confused of information overloading problem. The recommendation system is designed
based on personal preferen ces. It can recommend the fittest information to users, and it would help users to obtain in-
formation more conveniently and quickly. In our research, we design a recommendation system based on personal on-
tology and collaborative filtering technologies. Personal ontology is constructed by Formal Concept Analysis (FCA)
algorithm and the collaborative filtering is design based on ontology similarity comparison among users. In order to
evaluate the performance of our recommendation system, we have conducted an experiment to estimate the users’ sat-
isfaction of our experiment system. The results show that, combining collaborative filtering technology with FCA in a
recommendation system can get better users’ satisfaction.
Keywords: Document Recommendation System, Personal Ontology, Formal Concept Analysis (FCA), Collaborative
Filtering
1. Introduction
With the internet technology has been widely used in
human life, huge amounts of websites have been built
and updated every day. This phenomenon usually makes
the internet users at a loss in such a huge amount of in-
formation, and this problem is known as “information
overloading”. Furthermore, the information that hides in
the databases is beyond the search engines’ reach. In this
case, although many internet search engines are available,
it is still useless to information users to find what they
want. Therefore, many websites, such as Yahoo! news
and Amazon online bookstores, launch their own rec-
ommendation service on their platforms. They hope their
systems could recommend products or information to
users automatically and help users to find what they are
searching for more quickly. In advance, the recommen-
dation systems could even assist in answering to the po-
tential information in which the users are interested.
Collaborative filtering technology is considered to be
an effective way to solve the information overloading
problem [1]. This technology mainly emphasizes on the
cooperation between people. Th e system first collects the
information of the users and then calculates the similari
ties among the users. Through this way, the system could
learn the preferences of every user and those preferences
in common which could be recommended to the users. It
will not only present the information that the users are
interested in, but also some potential information that
may surprise the users. Currently, some famous websites
such as Amazon have adopted this technology. This
shows that among these recommendation systems, col-
laborative filtering technology is relatively successful
and most commonly used, as well as an excellent system
used in electronic commerce [2–4].
Apart from helping finding the demanded information,
the recommendation system aims to help the users to
search with a faster speed and accuracy by constructing
the shared documents and common preferences. It also
makes the resources and services on the internet easier to
access and share [5]. In this research, we integrate on-
tology and collaborative filtering to design a system to
provide information recommendation service. We adopt
Formal Concept Analysis (FCA) to construct a personal
ontology to show the conceptual structure of personal
preferences. FCA technology has been proved to be
helpful in the development of ontology [6–10].

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System323
This research is not only engaged in constructing a
recommendation system which combines ontology with
the collaborative filtering technology, but also compare
the users’ satisfaction with the system without that tech-
nology. We have developed a prototype system and con-
ducted a laboratory experiment to evaluate the users’
satisfaction on different recommendation mechanism.
The remainder of the paper is organized as the following.
Section 2 reviews major literature concerning recom-
mendation systems, ontology and FCA. System architec-
ture and experiment design are shown in Section 3, and
data analysis results are discussed in Section 4. Finally,
implications and conclusions are described in Section 5.
2. Literature Review
2.1 Recommendation Systems
At the present time, recommendation systems hold more
extensive definitions. It can be used to describe personal
recommendations from any system or direct the users to
find interested or useful targets from multiple possible
choices. In this information overloading era, the design
and development of recommendation systems is virtually
more attractive than search for information depend ing on
individuals, because it could help people make decisions
from the complicated information. Currently, recom-
mendation systems are already included in some elec-
tronic commerce websites such as Amazon [3].
The earliest recommendation system, developed by
Goldberg et al. [11] is called Tapestry. It filters the
useful information by collaborative filtering system.
Collaborative recommendation system is the most fa-
mous and commonly used one. The system analyses the
behaviors or preferences from the set of users within the
system. It finds out the set of users with similar charac-
teristics and takes this relevance as an evidence to in-
duct the potential preferences of the users. Therefore,
besides recommend the interested information to the
users, this research is expected to recommend the in-
formation that may arouse the users’ potential demands.
In our recommendation system, it will first collect the
users’ information and calculate similarities of every
user. From this way, the system could learn the prefer-
ences and the ones in common and find out the users
who hold the similar preferences.
2.2 Ontology
Ontology could be defined from many aspects. Schreiber
et al. [12] defined it as ontology provides a clearly de-
scription and con ceptualization to express the knowledge
in knowledge base from the aspect of knowledge base
construction. In additio n, Bernaras et al. [13] agreed that
ontology provides a clear description to conceptualize
knowledge in knowledge base. William and Austin [14]
also proposes that ontology is a set describing or ex-
pressing concepts or terms of a certain field and can be
used to organize the higher level of conceptual knowl-
edge in knowledge base or describe the knowledge of a
certain field. The process of its development leads to
different definitions of ontology, but one point in com-
mon is ontology could help describe knowledge and the
conceptual structure. In addition, the importance of on-
tology is that it matters the expression of knowledge
structure and the analysis through ontology so as to pre-
sent a clear knowledge structure. In one certain field,
ontology is the core of expressing knowledge system and
would help effectively express through analysis of on-
tology.
Therefore, the utmost task is to develop terms and re-
lations that could effectively express knowledge so that
the certain field or category would be analyzed effi-
ciently. Moreover, the development of ontology would
help share the knowledge. Knowledge base could be
constructed according to different circumstances due to
the share of ontology. For example, different manufac-
turers could use common terms and grammars to con-
struct and describe the catalog indexes of some product,
and then they share and use these indexes in automatic
data exchanging systems. This kind of sharing could
greatly increase the chances of knowledge reusing [15].
Now that ontology could familiarize the users with
knowledge in specific field, users could utilize the con-
ceptual correspondence of ontology to avoid the confu-
sion of conceptions and rapidly find conceptual cate-
gory in individual ontology. This could make browsing
websites and searching information more efficient and
convenient [5 , 16].
2.3 Formal Concept Analysis and Ontology
Formal Concept Analysis (FCA), proposed by Rudolf
Wille in 1982, is a data analysis theory to disclose con-
ceptual structures from data set [17]. The characteristic is
that structures of data set could produce the graphical
visualization, especially the quantitative analysis that the
social sciences cannot be fully captured. Ganter and
Wille [18] consid ered that FCA could mainly be used on
data analysis such as investigate and process definite data.
This data is based on Formal Abstractions of Concepts
which is prominent and understandable. Wille [17] com-
bined the target, property and relevance (each target
possesses a property) together to present these relations
by mathematical definitions of Formal Context and de-
fine Form al Concept [19].
The goal of both ontology and FCA is to build con-
ceptual models of knowledge domain. FCA can be
viewed as a technology of ontology construction to ob-
tain structured data by concept lattices; it can be used as
foundation of developing ontology manually and auto-
matically by extracting concepts from the data set; it can
also be used to present the visualization of ontology and

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System 324
User’s Preferen c e s
Keywords
Preferences Collecting Module
Weights Similarity Comparison
Collaborative Filtering Module
The most similar
user’s preferences
User’s New Preferences
Formal Context
Ontology Cons tr ucting M od ule
Concept Latt ices
Ontology Construction
Personal Ontology
Documents
(3)
(4)
Use
r
Read (1)
Recommend (6)
(2)
(5)
Figure 1. System architecture
help browse and analyze tasks. Among the theories com-
bining FCA and ontology, the most prominent applica-
tion is to iden tify the concept of ontology through fo rmal
concept [20]. Moreover, Hsu [7] proposes to automati-
cally construct on tology based on FCA theor y. It firstl y
extracts terms that stands for document concepts from
term extraction system. Then integrate the binary matrix
of document and terms to express independent, inter-
laced and inherited relations among different concepts
and form the diagram of relations of concepts of ontol-
ogy. The above documents all consider the property of
FCA as the concept of ontology and the other relevant
concepts as properties. Based on this view ontology is
constructed or combined. The researches mentioned
above prove that FCA and the concept of ontology
could effectively help construct ontology. This research
will use the ontology construction by FCA in recom-
mendation systems.
3. System Architecture and Experiment
Design
In this research, we aims to develop a recommendation
system based on the combination of collaborative filter-
ing technology and ontology. It will not only construct
personal ontology with the FCA, but also calculate the
users’ familiarity to the keywords of all the documents.
The users will give scores on those they read and are
interested in while browsing them. These scores could
show the users’ preferences and work as a weights stan-
dard in the construction of ontology.
3.1 System Architecture
Figure 1 shows the system architecture of our recom-
mendation system. In the step 1, the users enter the sys-
tem, and the system assigns 20 documents randomly to
users. The users browse and choose the top five docu-
ments they prefer to and give scores from 1 to 5 on the
familiarity of the keywords of the 5 documents. In the
step 2, the system analyze the collection of keywords and
scores in the preference documents and make weights
computing in users’ preference collection module to
prepare for the ontology construction and similarity
comparison. In the step 3, with the weights computed in
the previous module, the collaborative filtering module
will compare the keywords and weights of preference
with others. For the sake of time and efficiency, the sys-
tem will only compare the first 100 users in the database
and find the users with the highest similarity. The pref-
erence keywords and weights of these couple users will
be sent to users’ ontology construction module to prepare
for the ontology construction. In the step 4, the system
intermix the keywords and weights of the user with the
highest similar one’s. The sum of the keywords and
weights will be used to construct the users’ preference
ontology by ontology construction module based on FCA
technology. In the step 5, the system will send the new
personal preferences back, and then the system will cal-
culate the weights of each document. Finally, in the step
6, after calculating weighs of each document in the data-
base, the system recommend the top five documents with
the highest weights, and measure the user’s satisfaction
by online questionnaires.
The major modules in the system architecture are
shown as follows.
1) Document database
The experimental system recommends documents to
the users to read . In the do cument d ataba se, ther e are 210
mater dissertations focus on electronic commerce se-
lected from Electronic Theses and Dissertations System
in Taiwan1. The data schema of documents database is
composed of eleven fields, including serial number, au-
thor’s name, year, paper’s title, affiliation, abstract, and

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System325
five keywords.
2) Preferences collection module
For constructing personal preferences ontology by
FCA, we need to collect user’s preferences of keywords
of documents. We believe that choosing their preference
documents of the users cannot fully reflect the degree of
their preferences. Therefore, we propose the scoring
mechanism of the keywords to modify the weights be-
tween the concepts in the process of constructing ontol-
ogy. In this module, user should select 5 preferred docu-
ments and score from 1 to 5 for each keyword in the
documents to show their preference degree.
3) Collaborative filtering module
For the collaborative filtering mechanism, our system
should have some users’ preferences first. Therefore,
when a user enters our system, the system can select the
fittest user from the database and finish the collaborative
filtering. In our experiment, we collect 105 participants’
preferences in the database before collaborative filtering
mechanism is running.
To find the fittest user from the database, we need a
function to calculate the similarities between the users.
We define Sims as the degree of the similarities of two
users’ preferences, and its function is shown as follows.
1
11
r
t
mn
ij
KtWt
Sims
K
iWi KjWj






1
m
i
K
iWi

: the sum of weights of user’s preferred con-
cepts
1
n
j
K
jWj

: the sum of weights of the other user’s pre-
ferred concepts
1
r
t
K
tWt

: the sum of weights of the two users’ con-
junctive preferred concepts
4) Ontology construction module
This module mainly focuses on the weights of key-
words collection and constructs the personal ontology.
We adopt FCA [17] construct ontology. The steps are as
follows.
Step 1: produce the formal contexts of the documents
and keywords.
We first extract the collection of the keywords of the
chosen documents from the document database. Then we
match all the documents with the keyword s collection. If
the document includes certain keyword it will be marked
as “1”. In this way form the formal contexts of the
documents and keywords. Because of the scoring
mechanism in this research, the keywords collection will
be sequenced according to the weights of the users. In the
later part the preference discussion will be transformed
into the section of tree framework and make the concepts
with high weights as higher hierarchy. According to the
definition of FCA, this research defines the definition of
formal contexts as K, the document collection on
e-commerce as E, the keywords collection as T and the
binary of the document collection and keywords collec-
tion as R. Then their relation can be put into
:,,
K
ET R.
Step 2：Produce all the concepts C
Define A as the subset of E and B as the subset of T,
that is,
A
E, . If a certain concept is BT
A
B,
then it is marked as concept c (A, B). For a concept c
(A,B), if all the relations R between A and B can form a
biggest matrix, then all the collection of concept c is
marked as C.
Step 3: produce the concept lattices between all the
concepts
If the collection of all th e documents with the k eyword
B1 is includ ed in the collection of all the d ocuments with
the keyword B2, the keyword B1 is marked as the
sub-concept of the keyword B2. That is, for all the con-
cepts C, if , then is the sub-con cept of
and expressed as
1
BB
)
2111
(,)cAB
(,
222
(,cAB112 2 )(, )
A
BAB. The
sign

stands for hierarchy of concepts.
Step 4: transform into tree diagram of ontology
While transforming the concept lattices diagram into
tree framework of ontology by using breadth-first search,
the relations of nodes may be fairly complicated and
make the system spend too much time computing. This
would lead to the inefficiency of recommendation and
failure to promptly recommend documents to users. In
order to avoid this, while constructing concept lattices,
this research does not take the interlaced relations into
account and make the concepts with high weights higher
hiera rch y. Th en th e re lation contains only the concepts of
higher hierarchy and the lower hierarchy. Then by
breath-first search transform the relevance of formal
contexts into tree framework which is the users’ prefer-
ence ontology.
3.2 Experiment Design
This experiment aims to recommend the users documents
through two different recommendation systems and test
their satisfaction. First, to be the experiment group, this
system constructs ontology with the FCA theory, the
scoring system and collaborative filtering technology.
The other one, to be the control group, this system con
structs ontology with the FCA theory and the scoring
system without collaborative filtering. We will introduce
1http://etds.ncl.edu.tw/theabs/index.html

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System 326
Table 1. User’s satisfaction measurement
1. Do the recommendation documents meet your demands?
2. Are the recommendation methods accurate?
3. Are the recommendation methods satisfying?
4. Do you understand the recommendation methods?
5. Do you think the recommendation methods practical?
6. Do you think the recommendation methods reliable?
7. Do you think the recommendation methods clear?
8. Do you think the way of recommending understandable?
the recommendation steps of the first system as follows:
Step 1: Enter into the system: the users first read the
introduction of the first page to learn the purpose and
contents of the experime nt .
Step 2: Assign documents randomly: the system ex-
tracts 20 documents randomly from the 210 ones for the
users to read.
Step 3: Choose the documents the users prefer to and
give scores: the users click the 20 ones to further read the
contents and give scores on five interested ones. The
system will store the keywords co llection and preference
scores of the five documents to prepare for the comput-
ing or collaborative filtering of th e preferences.
Step 4: Ontology constructing for the users and rec-
ommends 5 do c uments to users based on ontology.
Step 5: After reading the recommendation documents,
the users could fill in the questionnaires. The satisfac-
tion refers to the satisfaction with information quality.
The users should answer eight questions with Likert’s
five point scale from very dissatisfying to very satisfy-
ing. The experiment finishes after the users answer
these questions.
4. Data Analysis
To evaluate the user’s satisfaction on our experiment sys-
tem, we have conducted a laboratory experiment research.
The system combining personal ontology and collabora-
tive filtering is served as the experiment group, and the
system that has only personal ontology recommendation
without collaborative filtering is served ad the control
group. There are totally 250 qualified participants have
been invited to the experiment. By randomly dispatched
by the system, 145 samples are assigned for experiment
group and 105 for control group. User’s satisfaction is
measured by questionnaires online. The questionnaire is
designed based on DeLone and Mclean’s IS (information
systems) success model [21,22]. This model proposes a
comprehensive perspective to measure the success of an
information system and has been widely used to appraise
the quality of information systems. In a nutshell, a suc-
cessful information system should have qualified infor-
mation quality and system quality to satisfy the users. In
our research, due to both the experiment group and con-
trol group are conducted in the same platform, the system
quality are the same in certain. We only adopt the meas-
urements for information quality in our questionnaires.
Table 1 shows the user’s information quality satisfaction
measurements and Likert’s five point scale, from very
disagree to very agree, is applied.
Factor analysis is applied to evaluate the validity of
our measurements. The KMO value of this construct is
0.856. It shows that these measurements are feasible to
factor analysis. Extract the dimensions whose eigenvalue
is larger than 1 by using principal component analysis
and orthogona l rotation thro ugh VARIMAX. A fter factor
analysis, we divide the eight questions into two factor
components. Question 2, 1, 6, 3 make up the first factor
component, and this construct is named as satisfaction
with recommendation results. Question 8, 4 and 7 make
up the second one, and is named as satisfaction with
recommendation process. Question 5 has the similar fac-
tor loading in both the two components (both are more
than 0.5). We w ould delete question 5 after factor analy-
sis.
Table 2 shows the descriptive statistics results of our
experiment. The experiment group always gets higher
satisfaction both on recommendation results and process.
To verify the experiment group really gets higher us-
ers’ satisfaction than the control one in statistics, the in-
dependent-samples T test is applied. The results are
shown in Table 3. No matter on recommendation results
or process, users get higher satisfaction significantly.
That is to say, the recommendation system based on the
combination of ontology and collaborative filtering sys-
tem is more satisfying than the one based only on per-
sonal ontology.
The higher satisfaction of experiment group might
cause by the extension capability of combining collabo-
rative filtering results with personal preferences. Due to
the original personal on tology is built based on only fiv e

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System 327
Table 2. Descriptive statistics results of researc h co nstr uc ts
Users’ Satisfaction Group N Mean Std. Dev.
Experiment 145 3.641 0.591
Control 105 3.441 0.580 Recommendation Results
Total 250 3.557 0.594
Experiment 145 3.740 0.658
Control 105 3.578 0.526
Recommendation Process
Total 250 3.672 0.610
Table 3. Independent-samples t te st re sults
Levene's Test for
Equality of Variances T-test for Equality of Means
Users’ Satisfaction
F Sig. t Sig.
Recommendation
Results 0.011 0.916 2.673 0.008**
Recommendation
Process 1.841 0.176 2.092 0.037*
interested documents, it will not include all the user’s
preferences certainly. Collaborative filtering would help
to capture user’s other preferences that have not been
defined in the original personal ontology. In the other
words, the expanded personal ontology, combining col-
laborative filtering results, might cover the potential
preferences that have not been discovered. Therefore, the
system recomme nds documents to users base d on t he exp-
anded perso nal ontology wo uld cause higher satisfacti on.
5. Conclusions
This research is expected to take advantages of collabo-
rative filtering and personal ontology to design an effec-
tive recommendation system. Therefore, we have first
discussed how to construct personal ontology based on
one self’s and others’ preferences. The personal ontology
is built up by FCA method, in advance, we used scoring
mechanism to intensify the weights of users’ preferences.
Then, we elaborated on how to make use of this method
to provide personal recommendation service in an elec-
tronic documents repository system. We have imple-
mented a prototype system and conducted a laboratory
experiment to evaluate the system’s performance. The
research results show that the users have higher satisfac-
tion with the recommend ation system that combined col-
laborative filtering and ontology technology.
In practice, this research applies collaborative filter-
ing and ontology to provide personal recommendation
service on an electronic documents website. This per-
sonal recommendation method can be used widely in
different online websites such as electronic news web-
site, or e-retail website to recommend news/products to
customers.
However, in our experiment, the recommendation ser-
vice is built based only 210 master theses. Due to the
FCA method should calculate the relations among each
document, it might cause performance problem when it
were used in real repository system that usually h as more
than ten or hundred thousands of documents. The FCA
method should be improved in calculation efficiency
when it is used in the larger scale system. In the other,
how to extract the proper keywords from documents
would be another important and interesting issue. In our
system, the recommendation documents database is
composed of master theses. As usual, the maser theses
have accurate keywords that are defined by the author.
However, in some other documents repository system,
such as news website, there is no well-defined keyword
in the system. How to extract proper keywords from this
kind of system would be anther critical problem when
our recommendation system is implemented.
REFERENCES
[1] B. Sarwar, J. Konstan, A. Borchers, J. Herlocker, B.
Miller, and J. Riedl, “Using Filtering Agents to Improve
Predictio n Quality in the GroupLens Researc h Collabora-
tive Filtering System,” Proceedings of the 1998 Confer-
ence on Computer Supported Cooperative Work, pp.
345–354, 1998.
[2] M. Balabanovic and Y. Shoham, “Fab: Content-Based,
Collaborative Recommendation,” Communications of the
ACM, Vol. 40, pp. 66–72, 1997.
[3] J. B. Schafer, J. Konstan, and J. Riedl, “Recommender
System in E-Commerce,” Proceedings of the first ACM

Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System 328
Conference on Electronic Commerce, pp. 158–166, 1999.
[4] S. Staab, and H. Werthner, “Intelligent Systems for Tour-
ism,” IEEE Intelligent Systems, Vol. 17, pp. 53–66, 2002.
[5] L. Khan, D. McLeod, and E. Hovy, “Retrieval Effective-
ness of an Ontology-based Model for Information Selec-
tion,” Very Large Data Bases, Vol. 13, pp. 71–85, 2004.
[6] H. M. Haav, “A semi-automatic method to ontology de-
sign by using FCA,” Proceedings of the 2nd International
CLA Workshop, Ostrava, pp. 13–25, 2004.
[7] C. H. Hsu, “Automatically Constructing Ontology on
Semantic Web,” M.S. thesis, Fu Jen Catholic University,
Taiwan, 2004.
[8] M. Obitko, V. Snasel, J. Smid, and V. Snasel, “Ontology
Design with Formal Concept Analysis,” In V. Snasel, and
R. Belohlavek, (eds.) Concept Lattices and their Applica-
tions, Ostrava: Czech Republic, pp. 111–119, 2004.
[9] G. Stumme, and A. Maedche, “FCA-MERGE: bottom-up
merging of ontologies,” Proceedings of the Seventeenth
International Conference on Artificial Intelligence, pp.
225–234, 2001.
[10] Y. Zhao, X. Wang, and W. Halang, “Ontology Mapping
based on Rough Formal Concept Analysis,” Proceedings
of the Advanced International Conference on Telecom-
munications and International Conference on Internet and
Web Applications and Services, pp. 180, 2006.
[11] B. Ganter, and R. Wille, “Applied lattice theory: Formal
Concept Analysis,” In G. Grater, (edition), General Lat-
tice Theory, Springer, Berlin, 1997.
[12] G. Schreiber, B. Wielinga, and W. Jansweijer, “The kac-
tus view on the “o” word,” Workshop on basic ontologi-
cal issues in knowledge sharing: international joint con-
ference on artificial intelligence, pp. 159–168, 1995.
[13] A. Bernaras, I. Laresgoiti, and J. Corera, “Building and
reusing ontologies for electrical network applications,”
Proceedings of European Conference on Artificial Intel-
ligence, pp. 298–302, 1996.
[14] S. William and T. Austin, “Ontologies,” IEEE Intelligent
systems, Vol. 14, pp. 18–19, 1999.
[15] B. Chandrasekaran, J. R. Josephson, and V. R. Benjamins,
“What are ontologies, and why do we need them?” IEEE
Intelligent systems, Vol. 14, pp. 20–26, 1999.
[16] J. Chaffee and S. Gauch, “Personal Ontologies for Web
Navigation,” Proceedings of Conference on Information
Knowledge Management, pp. 227–234, 2000.
[17] R. Wille, “Restructuring lattice theory: An approach
based on hierarchies of concepts, ” In I. Rival, (edition) Or-
dered Sets, Reidel, Boston-Dordrecht, pp. 445–470, 1982.
[18] A. Formica, “Ontology-based concept similarity in For-
mal Concept Analysis,” Information Sciences, Vol. 176,
pp. 2624–2641, 2006.
[19] K. E. Wolff, “A first course in Formal Concept Analysis -
how to understand line diagrams,” Advances in Statistical
Software, Vol. 4, pp. 429–438, 1994.
[20] P. Cimiano, A. Hotho, G. Stumme, and J. Tane, “Concept
Knowledge Processing with Formal Concept Analysis and
Ontologies,” Proceedings of Second International Confer-
ence on Formal Concept Analysis, pp. 189–207, 2004.
[21] W. H. Delone and E. R. Mclean, “Information System
Success: The Quest for the Dependent Variable,” Infor-
mation Systems Research, Vol. 3, pp. 60–95, 1992.
[22] H. D. William and R. M. Ephraim, “Information Systems
Success: The quest for the dependent variable,” Informa-
tion Systems Research, Vol. 3, pp. 60–95, 1992.