A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING
An Academic Case Study and Evaluation
Richard Gil, Leonardo Contreras
Dept. of Processes and Systems, Simón Bolívar University, Caracas-Baruta, Venezuela
María J. Martín-Bautista
Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Keywords: Ontology Learning, Methodology, Systemic, Methodology Evaluation, Tools, Academic Domain.
Abstract: There is an important dispersion of technical and methodological resources to support the complete
Ontology Learning (OL) process from diverse knowledge sources. This fact makes the maintaining of the
structures of representation (ontologies) difficult. Therefore, the Knowledge-based Systems associated with
user’s domains may not fulfil the increasing knowledge requirement from the user. In this paper, we give a
possible solution for this problem. For this purpose, we propose a Systemic Methodology for OL (SMOL)
that unifies and simplifies to the users the whole process of OL from different knowledge sources
(ontologies, texts and databases). SMOL as methodology is evaluated under DESMET methods, in addition
with their application for an academic case study is also included.
1 INTRODUCTION
Reaching the knowledge from a semantic techno-
logical perspective has propitiated the development
of new technical methodologies and resources.
Through these product-tools obtained the society
should explore, discover, recover, store and update
knowledge associated to some specific domains
(Decker et al., 2000)(Nonaka, 1994).
The aim is not only to obtain system products to
support user requirements that may be ’devalued’
throughout time, but also to reach Knowledge-based
System (KBS) able to auto-learn and to make reco-
mmendations and learning actions related to
different user communities (Borges et al., 2008)
(Garruzzo et al., 2007).
However, this kind of systems is not so easy to
develop and maintaining (Abdullah et al., 2006).
This is due several aspects: first, the ontology
engineering methodological resources are in
maturation process yet; second, even though
technologies for handling knowledge based on
ontologies satisfy some user requirements, they
cannot guarantee a complete quality-driven and
user-oriented development during the ontology
engineering process; and finally, partial experiences
of ontology engineering developers, researchers and
users may be not incorporated as part of the tacit
knowledge (behaviour and skills) in the new metho-
dologies and technologies yet (Gómez-Pérez and
Manzano-Macho, 2005) (Haase et al., 2005).
Despite there are various definitions about OL,
we are in according to (Gómez-Pérez and Manzano-
Macho, 2005), where it is “the application of a set of
methods and techni-ques used for improving a
previous ontology with heterogeneous Knowledge
Sources (KSo), avoiding the complete Ontology
Development process”. These sources can be
ontologies previously developed, texts, database or
results of a process of ontology integration
(Maedche and Staab, 2001).
Precisely the widespread variety of mechanisms
and resources for OL make difficult the definition of
a standard methodology for OL. Consequently, in
(Gil, 2009) a new Systemic Methodology for OL
(SMOL) is conceived and proposed to overcome
some identified restrictions.
In this paper, we focus on the OL processes in
Section 2. The systemic methodology perspective is
showed in Section 3. SMOL methodology is descri-
206
Gil R., Contreras L. and J. Martín-Bautista M..
A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation.
DOI: 10.5220/0003070602060212
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2010), pages 206-212
ISBN: 978-989-8425-29-4
Copyright
c
2010 SCITEPRESS (Science and Technology Publications, Lda.)
bed in Section 4. A case study in the academic
domain applying SMOL is included in Section 5. A
general methodology evaluation is applied to SMOL
in Section 6, and finally, conclusions in Section 7.
2 ONTOLOGY LEARNING
There are several methodological alternatives in the
literature about OL. The one suggested in (Maedche
and Staab, 2001) includes some learning approaches
besides possible and recommended set of activities
associated with them. On the other hand, in (Gliozzo
et al., 2007), a different classification of the recom-
mended techniques into two groups are given. The
first group includes those approaches that allow get-
ting knowledge and retrieving information from
electronic texts. The second group, includes those
approaches that allow to 'gain knowledge' based on
previous structured knowledge and ontologies such
as dictionaries and thesaurus (Gómez-Pérez and
Manzano-Macho, 2005)(Gacitua et al., 2008).
OL approaches according with the KSo are
three-fold: a) OL from other ontologies developed
previously (Ehrig, 2007)(Noy and Musen, 2000)
(Euzenat et al., 2007) b) OL from documents (Buite-
laar and Cimiano, 2008) (Cimiano, 2006). And c)
OL from database schemes and their data-values
(Astrova et al., 2007)(Nyulas et al., 2007) (Cerbah,
2009).
2.1 Ontology Learning Resources
There are some definitions regarding Methodologi-
cal Resources (MR) that allows us to understand the
concepts associated to MR and to avoid confusions
that sometimes happen in technical literature. The
following definitions (Callaos, 1992) have been
considered: a) Techniques: subjective capabilities
(abilities or skills) to handle a tool properly. b) Me-
thods: a way of thinking or doing using a tool to
achieve an objective. c) Tools: objective capabilities
to use the resources properly to apply techniques.
And, d) Methodologies: a related set of methods,
techniques and tools which could be used for
reaching objectives.
2.2 Ontology Learning Problematic
Although important technical advances in MR in the
OL field, according to each KSo, have demonstrated
the main OL strengths and opportunities, authors
recently have reported high dispersion and little
integration among those MR producing OL results
from the same KSo.
Therefore, to synthesize the general OL
problems a situational technical analysis, which is
known as SWOT (Strengths, Weaknesses,
Opportunities and Threats), has been used (Hill and
Westbrook, 1997). This technique simplifies the OL
understanding from two broad perspectives. First, it
addresses the knowledge development and
reconstruction as an OL process and, secondly, by
studying it in terms of the resulting semantic (Noy
and Klein, 2004).
In agreement with (Gómez-Pérez and Manzano-
Macho, 2005) and (Shamsfard and Abdollahzadeh,
2003) some conclusions associated with those
studies about OL methodologies can be summarized.
Regarding OL Methods: a) There is not an esta-
blished standard. b) The methods are not usually
combined, and c) Many methods are not associated
with specific tools. With regard to OL Tools: a) All
of them help to extract knowledge; b) A small group
of them allow the retrieval of a complete taxonomy,
and c) Only some tools support specific OL
methods.
It is possible to infer also, that OL methodology-
cal options do not exist as a complete integral,
unified and dynamic way to face the OL problems
for knowledge recovery from heterogeneous KSo.
3 SYSTEMIC PERSPECTIVE
Methodological options used to get designs and
knowledge product development (systems, models
or ontologies) are associated to strategies and pro-
cesses structured in some way. Many approaches
closer to Software Engineering reflect the efforts
dedicated in this direction (Sommerville, 2006).
More specific methodological approaches orient-ted
on one hand to Software Development (Press-man,
2006) and on the other hand, to Knowledge
Engineering (Gómez-Pérez et al., 2004) (Buitelaar
and Cimiano, 2008)(De-Nicola et al., 2009) the
previous methodological options have arisen.
The proposed methodological perspective tries to
conciliate the system development total quality
paradigms with user-centered services to attend their
demanded requirements. This conciliation is suppor-
ted by systemic methodologies instead of systematic
ones (Callaos and Callaos,2003): a) Systematic
methodologies are oriented to the efficiency, with a
predetermined behaviour, strict and closed. (e.g.
Structured Life Cycle) and b) Systemic methodolo-
gies are oriented to the effectiveness, with a non-
A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation
207
Figure 1: Systemic Methodology for Ontology Learning applied for an academic case study.
predetermined behaviour, flexible and open. (e.g.
Agile Process).
Applying a methodology to an environment (an
organization or system) is an evolutionary maturing
process to achieve results with approaches such as
the Action-Research (Baskerville, 1999) and the
Action-Learning (Dilworth, 1998). The action (Ac-
tion-Design) in both cases allow to support: first,
researching to discover; second, learning to unders-
tand and to experiment; and third, system design and
synthesis to generate new ideas and to solve specific
problems to satisfy certain requirements.
Indeed, the product (ontologies) and the process
(methodologies) must be developed in a trade-off
between efficient and effective action-design.
4 DESCRIPTION OF SMOL
The lack of integrated methodologies covering the
whole process of OL leads us to propose and
experiment with new methodological options. This
new systemic methodological proposal must be
flexible, iterative, incremental and adaptable to
normal users, experts and knowledge engineers
using some MR previously developed according the
quality approach cited (Callaos and Callaos, 2006).
Users of SMOL to combine MR for diverse KSo
in a proper way, considering the existence of a
domain ontology already elaborated for KBS which
could be improved through updating/enrichment OL
processes (Haase et al., 2005)(Noy and Klein, 2004).
For this methodology design, we select a frame-
work for knowledge retrieval of (Yao et al., 2007).
4.1 SMOL Phase-flow Description
The phase-flow of SMOL is proposed (some in
Figure 1), emphasizing the MR recommended to be
used in each specific phase. The activities related to
each original phase of SMOL are explained as
follow: I. Methodology strategy selection. The
complexity of the domain is evaluated based on the
availability of information/knowledge useful about
the domain (Zhou, 2007). The methodology strategy
is drafted/selected using an appropriated arrange-
ment of MR for each KSo relative to. II. Knowledge
discovery. The MR from different knowledge -sour-
ces and -repositories are combined. III. Query requi-
rements. Different queries are formulated to the KSo
available by browsers or other kind of applications.
IV. Knowledge selection. A selection of the retrie-
ved information from the formulated queries to the
sources and repositories is performed. V. Know-
ledge structures construction. Different structures
such as ontologies and contexts can be built interact-
tively with users’ advisory by ontology alignment,
machine learning techniques, etc. VI. Knowledge
exploring and searching. The knowledge structures
are explored, verified and validated and the search
can be refined. VII. Knowledge structures reorgani-
zation. Processes such as grouping of instances,
ontology population and other similar activities are
performed in this phase. And, VIII. Knowledge-
based System configuration. Users set-up the main
modules of the KBS that have ontologies updated
and associated with the users’ domain.
Other five activities were developed for SMOL
drafting: 1) Methodology strategic selection phase is
designed considering that the user of SMOL may
adjust MR according to the information available in
the KSo about the domain-complexity. 2) Knowled-
I- Methodology
s
trategy selection
(bottom-up
)
II- Knowledge
Discovery
(Scholar-Google)
III- Query
requirements
(Classes taxonomy)
IV-Knowledge
selection
(By Rapid-I)
V- Knowledge struc-
tures construction
(GATE-Ontology)
VII-Knowledge struc
-
ture reorganization
(Integration DEA+Text)
VI- Knowledge ex-
ploring & searching
(+Text)
c
Knowledge
structure updated
c
Satisfied requirements
b
a
Strategy selected
a
b
Texts
Structured
knowledge
Knowled
g
e sources
User/Task Profiles
Users
Ontology-DEA+Texts
Phase
Decision point
Phases Flow
Data flow
Storage-DB
KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development
208
ge Sources are configured as storage component
(DB) with the purpose of knowledge reusing. 3)
User/Task profiles are configured as storage compo-
nents to queries-operations registration (log) with
the purpose of reusing MR and recommending tasks.
4) Decision points have been included for user cycli-
cal-quality-check purpose. Some of them are shown
–as rhombus- in the Figure 1. And, 5) A methodolo-
gical Phase-flow activities description is detailed as
input, output, methods and tools recommended
according with each KSo and strategy selected.
5 ACADEMIC CASE STUDY
The objective of this study in the academic domain
is to retrieve and to add new knowledge into an
ontology named Ontology-DEA previously develo-
ped for a Decision-Support System of a University
of Venezuela (Ramos and Gil, 2007). In this experi-
mental case, it was improved by users the ontology-
DEA (incremental/iterative) with knowledge extrac-
ted from a corpus of texts. SMOL application for
this case is shown in Figure 1 (Gil et al., 2009).
A bottom-up learning strategy (Phase I) was
draf-ted and selected considering the following key
acti-vities: a) Finding and selecting a set of texts
from Internet with experts-users advisory. b)
Identifying from the corpus, some relevant keywords
by agent for ontology updating. And, c) applying the
OL from texts via text annotations and ontology
population.
Texts selection with user’s participation is
carried out in these Phases (II & III ). Users
recovered texts for the corpus, through Google
Scholar. From an initial set of 1000 retrieved texts, a
final set of 480 texts were selected using a file-
length base.
The learning agent developed in RAPID-I with
the plug-in WVtool was used to classify texts by
their relevant keywords, so they could be added to
the corpus for future updating (Phases III & IV). The
used technique is “text clustering” with the TF-IDF
term weighting scheme. Moreover, different pro-
cessses of tokenization, stop-word removing and
stemming were performed. Keywords found by the
agent were: “accredit, style, programming, distance,
institute, program, online, faculties, course and
student”. Those Keywords selected by the agent
were inputs to the next process in GATE via Onto
Gazetteers (Phase VI). The central purpose was to
identify representative terms and concepts in the
texts of the corpus besides corresponding Gazette-
ers’ annotation standard (e.g. dates or places).
An ontology graphical tool option for ontology
management was used in GATE to display annota-
tions to the users and to help them to support ontolo-
gy updating (evolution) from texts (Phases V &VII).
Other SMOL applications for the same case
study using other KSo have been reported: a) OL by
comparing to domain ontology located and
recovered from the Internet (Gil et al., 2008). And,
b) relevant knowledge about profiles of professor’s
subdomain from a relational database (RDB) of
another University was obtained (Gil et al., 2010).
6 SMOL EVALUATION
There are not so many alternatives for methodology
evaluations applied to the Ontology Development
field. One of the most referred in the Software Engi-
neering area is DESMET (Kitchenham, 1996).
We have used a combination of these DESMET
methods: a) Screening: A feature-based evaluation
done by a single individual who not only determines
the features to be assessed and their rating scale but
also does the assessment. For initial screening, the
evaluations are usually based on literature describing
the software method/tools rather than the actual use
of the methods/tools. b) Experiment: A feature-
based evaluation done by a group of potential users
who are expected to try out the methods/tools on
typical tasks before making their evaluations. And,
c) Case study: A feature-based evaluation performed
by someone who has used the method/tool on a real
project.
Those methods are recommended by DESMET
to be used when: a) Large number of methods/tools
to assess. b) Short timescales for evaluation exercise.
c) Benefits difficult to quantify. d) Benefits
observable on a single project. e) Stable
development procedu-res. f) Relatively small
learning time. And, g) Tool-/method user population
very varied and limited.
6.1 Qualitative Screening
To apply the Qualitative screening of the DESMET
evaluation, we have followed the two-fold: First, we
have performed below an interesting evaluation
approach applying usability/suitability criteria
assessment to evaluate by comparing our proposal
(Dahlem and Hahn, 2009).
On the other hand, we have developed a short
comparison of SMOL among two similar OL
methodologies published recently by (Simperl et al.,
2008) and (Novacek et al., 2007).
A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation
209
6.1.1 Screening through Usability Criteria
Dahlem’s usability and suitability evaluation pro-
posal (Dahlem and Hahn, 2009) has considered the
thirteenth methodological criteria suggested and
applied. Those are: Adequate terminology (C1);
Structure (C2); Descriptiveness (C3); Transparency
(C4); Error avoidance (C5); Robustness (C6);
Lookahead (C7); Consistency (C8); Hiding forma-
lity (C9); Expressiveness (C10); Conceptualization
flexibility (C11). Ontology assumptions (C12); and,
Tool support (C13). These criteria are combined
originally in an upper level under the following
terms: I) Learnable. II) Efficiency. III) Memo-
rability. IV) Error Handling. And, V) Satisfaction.
First, we assessed the SMOL methodology cha-
racteristics with the usability criteria cited. As result,
SMOL has up to nine of thirteen representative
criteria for methodology usability according to
Dahlem’s proposal. According to the total usability
evaluation criteria (uniform presence considered),
Efficiency and Satisfaction for SMOL, the value is
high (0,85 & 0,75). As for the Learnable, Memora-
bility and Error-handling criteria about SMOL are
medium (0,5). Indeed, these methodology evaluation
results about SMOL show comparative feature-
/capabilities among other equivalent methodologies.
6.1.2 Screening through Comparison
Comparing the SMOL methodology among the
proposal of Simperl et al. (Simperl et al., 2008) and
DINO (Novacek et al., 2007), there are some aspects
to point out. Mainly, SMOL has more elaborated
methodological options to support OL processes
from diverse KSo. The main options are: 1) SMOL
considers explicitly the assessment of the domain-
complexity characteristics for strategy selection.
And, 2) the OL strategy selection is based on an
approach of learning to start not always from texts,
but from other KSo such as databases and
ontologies, inclusive at beginning of the process.
Some details are omitted due paper-pages limit.
6.2 Qualitative Experiment And Case
The main way to test the SMOL functionality is
based on the case study, because we can check the
user validation and experiment with related me-
thods/tools. Some users were trained/familiarized
with some OL methods/tools used (e.g. Protégé,
Prompt-CogZ, Racer-Pro, GATE).
For each KSo, an evaluation strategy has been
designed considering: context (goals-constrains),
planning and design, preparation, execution, data
analysis, dissemination and decision-making.
An interview-questioner was given to the users
(up to 6) during the OL cycle according to each
KSo. A feature-based analysis was applied to those
results associated with those MR used. Particularly,
we asked them about Tools-functionality and Input-
/Output related to the OL methods/techniques
learned/applied.
The case study evaluation revealed the user satis-
faction about the SMOL methodology flexibility,
due to the capability of the MR integration in the
systemic component. A minor issue very interesting
for the user is the diversification of strategies to
reach knowledge aggregation from different KSo.
7 CONCLUSIONS
There is a lack of integrated methodologies in the
OL process, whatever the sources considered: onto-
logies, texts and database. A Systemic Methodology
for OL named SMOL has been designed considering
pros and drawbacks of the previous OL methodolgi-
cal proposals but including MR for diverse KSo. The
result is an integral, flexible, open, interactive and
iterative methodology user-oriented.
The SMOL methodology has been applied and
checked in an academic case study for different
KSo. Particularly, the OL from texts has been
detailed in this work.
The ontology updated by user’s participation
help us for SMOL validation. The SMOL
methodology evaluation as well as the preliminary
result obtained for this case study, reveal the
feasibility of SMOL as an instance of new
methodological perspectives for OL from texts, as a
way to update ontologies asso-ciated with KBSs of
the users’ domain.
In future works, promissory results could be ob-
tained with other SMOL cases applications combi-
ning incrementally some different KSo. Likewise,
other experimental and specific evaluations have
been performed to increase the SMOL background.
REFERENCES
Abdullah, M., Kimble, C., Benest, I., and Paige, R. (2006).
Knowledge-based systems: a re-evaluation. Journal of
Knowledge Management, 10 Nro 3:127–142.
Astrova, I., Korda, N., and Kalja, A. (2007). Rule-based
transformation of sql relational databases to owl
KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development
210
ontologies. Proceedings of the 2nd International
Conference on Metadata & Semantics Research.
Baskerville, R. (1999). Investigating information systems
with action research. Comm.AIS, v:2: Art 19.
Borges, A., Corniell, M., Gil, R., Contreras, L., and
Borges, R. (2008). Towards a study opportunities
recommender system in ontological principles-based
on semantic web environment. In The 4th
WSEAS/IASME. (EDUTE’08), ACM vol 8, no 2.
Buitelaar, P. and Cimiano, P. (2008). Ontology Learning
and Population: Briging the Gap Between Text And
Knowledge. IOS Press, Netherland.
Callaos, N. (1992). A systemic system methodology. In
International Conference on System Research
Informatic and Cybernetics, Baden-Baden, Germany.
Callaos, N. and Callaos, B. (2003). Toward a practical
general system methodological theory. Journal of
Systemics, Cybernetics and Informatics, 1:114–120.
Callaos, N. and Callaos, B. (2006). Designing with a
system total quality. In on Information System
Analysis, and Synthesis, ISAS’06, USA, p 15–23.
Cerbah, F. (2009). RDBToOnto User Guide, Version 1.2
Beta From relational Database to Fine-Tuned
Populated Ontologies. http://www.tao-project.eu/
Cimiano, P. (2006). Ontology Learning and Population
from Text: Algorithms, Evaluation and Applications.
Springer-Verlag New York, LLC.
Dahlem, N. and Hahn, A. (2009). User-friendly ontology
creation methodologies-a survey. 15th Amer. Conf.
On Information Systems, California-USA, pages 1–9.
De-Nicola, A., Missikoff, M., and Navigli, R. (2009). A
software engineering approach to ontology building.
Information Systems, Elsevier, 34:258–275.
Decker, S., Melnik, S., Van-Harmelen, F., Klein, D.,
Fensel, F., Broekstra, M., Erdmann, J., and Horrocks,
M. (2000). Knowledge networking the semantic web:
The roles of xml and rdf. IEEE Internet Computing.
Dilworth, R. (1998). Action learning in a nutshell.
Performance Improvement Quarterly, Vol 11(1).
Ehrig, M. (2007). Ontology Alignment: Biding the
Semantic Gap. Book, Springer-Verlag.
Euzenat, J., Mocan, A., and Sharffe, F. (2007). Ontology
Management, Ontology Alignments: An Ontology
Management Perspective. Book, Springer.
Gacitua, R., Sawyer, P., and Rayson, P. (2008). A flexible
framework to experiment with ontology learning
techniques. Know.-Based Syst., 21(3):192-199.
Garruzzo, S., Rosaci, D., and Sarné, G. (2007). Mars: An
agent-based recommender system for the semantic
web. LNCS, 4531:181–194.
Gil, R. (2009). New systemic methodology framework for
ontology learning (in spanish). Master’s thesis,
Dpto.Computer Science, Granada University, Spain.
Gil, R., A.Borges, Ramos, L., and Contreras, L. (2008).
Ontologies integration for university institutions:
Approach to an alignment evaluation. In Proc. 19th
Australian Conference on Software Engineering
ASWEC 2008, pages 570–578.
Gil, R., Borges, A., Contreras, L., and Martín-Bautista, M.
(2009). Improving ontologies through ontology
learning: a university case. In CSIE’09, IEEE
Computer Society, March-April, L.A.-USA.
Gil, R., Martín-Bautista, M., and Contreras, L. (2010).
Applying an ontology learning methodology to a
relational database: University case study. IEEE-
ICSC-2010, USA, Sep 22-24 (Accepted).
Gliozzo, A., C. Caracciolo, M. D-Aquin, M. S., Peter, W.,
Voelker, J., Dzbor, M., Mota, E., Gomez-Perez, A.,
Haase, P., Waterfield, W., Contreras, J., Grobelink,
M., Euzenat, J., Cunning, H., Staab, S., Gangemi, A.,
Angele, J., Iglesias, M., Lobo, T., and Lopez, A.
(2007). Results from experiments in ontology learning
including evaluation and recommendation. Technical
Report, http://www.neon-project.org/
Gómez-Pérez, A., Fernando-López, M., and Corcho, O.
(2004). Ontology Engineering. Book, Springer-Verlag,
London- UK.
Gómez-Pérez, A. and Manzano-Macho, D. (2005). An
overview of methods and tools for ontology learning
from text. Knowledge Engineer. Rev., 19:187–212.
Haase, P., Volker, J., and Sure, Y. (2005). Management of
dynamic knowledge. Journal of Knowledge Mana-
gement, 9:97–107.
Hill, T. and Westbrook, R. (1997). Swot analysis: It’s time
for a product recall. Long Range Planning, 30:46–52.
Kitchenham, B. (1996). Evaluating software engineering
methods and tool. part 3: Selecting an appropriate
evaluation method. ACM SIGSOFT Software
Engineering Notes, 21:9-12.
Maedche, A. and Staab, S. (2001). Ontology learning for
the semantic web. Intel. Systems, IEEE, 16:2:72-79.
Nonaka, I. (1994). A dynamic theory of organizational
knowledge creation. Organization Science, 5(1):14-37.
Novacek, V., Laera, L., and S.Handschuh (2007).
Semiautomatic integration of learned ontologies into a
collaborative framework. In Proceedings of
IWOD/ESWC, and ESWC 2007.
Noy, N. and Klein, M. (2004). Ontology evolution: Not the
same as schema evolution. Knowledge and
Information Systems, 6:428–440.
Noy, N. and Musen, M. (2000). Prompt: Algorithm and
tool for automated ontology merging and alignment.
Proceeding of National Conference On Artificial
Intelligence.
Nyulas, C., O’Connor, M., and Tu, S. (2007). Datamaster
- a plug-in for importing schemas and data from
relational databases into protege. 10th Intl. Protege
Conference, Budapest (2007).
Pressman, R. (2006). Software Engineering: A Practioners
Approach. Sixth Edition McGraw-Hill, New York
2006.
Ramos, L. and Gil, R. (2007). Propuesta de sistema de
información para apoyar la gestión de la educación a
distancia. CISCI’07, July. Orlando-USA.
Shamsfard, N. and Abdollahzadeh, A. (2003). The state of
the art in ontology learning: a framework for
comparison. Know. Enginer. Rev, 18 (4):293-316.
Simperl, E., Tempich, C., and Vrandecic, D. (2008).
A
Methodology for Ontology Learning. Chapter of Book
A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation
211
Ontology Learning & Population. IOS Press, Buitelaar
& Cimiano Eds.
Sommerville, I. (2006). Software Engineering. Pearson
Education.
Yao, Y., Zeng, Y., Zhong, N., and Huang, X. (2007).
Knowledge retrieval. In Proceedings IEEE/WIC/ACM
International Conference on Web Intelligence.
Zhou, L. (2007). Ontology learning: state of the art and
open issues. Information Technology and Manage-
ment, 8(3):241–252.
KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development
212
