An Automatic Approach for Bilingual Tuberculosis Ontology Based on Ontology Design Patterns (ODPs)

TELKOMNIKA, Vol.16, No.1, February 2018, pp. 282~289
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i1.6587  282
Received October 5, 2017; Revised December 10, 2017; Accepted December 29, 2017
An Automatic Approach for Bilingual Tuberculosis
Ontology Based on Ontology Design Patterns (ODPs)
Bambang Harjito*
1
, Denis Eka Cahyani
2
, Afrizal Doewes
3
1,2,3
Sebelas Maret University, Department of Informatics Faculty of Mathematics & Natural Sciences,
Surakarta, 57126, Indonesia
*Corresponding author, e-mail: bambang_harhito@staff.uns.ac.id
1
, denis.eka@staff.uns.ac.id
2
,
afrizal.doewes@staff.uns.ac.id
3
Abstract
Ontology is a representation term used to describe and represent a domain of knowledge.
Manually ontology development is currently considered complex, requiring a lot of time and effort. This
research was proposed to develop methods to build automatic domain ontology bilingual in Indonesian and
English by using corpus and ontology design patterns (ODPs) in tuberculosis disease. In this study, the
methods used were to combine ontology learning from text and ontology design patterns to decrease the
role of expert knowledge. The methods in this research consist of six stages are term and relation
extraction, matching with Tuberculosis glossary, matching with ODPs, score computation similarity term
and relations with ODPs, ontology building and ontology evaluation. The results of ontology construction
were 362 terms and 44 relations with 260 terms were added. The calculation accuracy of ontology
construction was 71%. Ontology construction had higher complexity and shorter time as well as decreases
the role of the expert knowledge which proof that the automatic ontology evaluation is better than manual
ontology construction.
Keywords: automatic, ontology building, ontology design patterns, tuberculosis
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Tuberculosis is a public health problem in the world. Tuberculosis (TB) is an infectious
bacterial disease caused by the microorganism Mycobacterium tuberculosis that affected the
human lungs but can also on the organ or other tissue such as skin, eye, lymph nodes, bone, a
lining of the brain and other organs [1, 2].
World Health Organization (WHO) estimated that 8.7 million new cases and 1.4 million
died of tuberculosis cases annually. Approximately 75% of patients Tuberculosis were in the
most productive age (15-50 years). Other than economic disadvantages caused by the lost of
annual income over the patient, tuberculosis had another negative impact such as social stigma
and even ostracized by the community. Indonesia was ranked fourth of the most amount of
tuberculosis cases in the world after India, China, and South Africa [3].
One way to prevent the growth of patients suffering from Tuberculosis disease is to
improve the quality of capable health workers to handle the tuberculosis disease situation. The
qualities of health workers can be improved by increasing their knowledge of tuberculosis cases
in the society. Increasing knowledge of health workers against disease will impact the health
services to be better for society.
By the development of technology, sources of knowledge about tuberculosis disease
can be obtained easily from textbooks, scientific journals, websites etc. Currently, there are
several websites which publish a collection of scientific journals on health, including
Tuberculosis in Indonesia, e.g. Health Science Journal of Indonesia and Makara Journal of
Health Research. These websites have hundreds of scientific journals related to health,
including the Tuberculosis disease that can be utilized to increase knowledge of health workers
in managing tuberculosis disease situation [4].
Scientific journals are a source of knowledge that is vital to develop research and
technology regarding the disease in Indonesia, including tuberculosis. Text in the scientific
journal can be used to build ontology in health, particularly tuberculosis disease. Ontology is a
representation term used to describe and represent a domain of knowledge [5]. Ontology as a

TELKOMNIKA ISSN: 1693-6930 
An Automatic Approach for Bilingual Tuberculosis Ontology Based on… (Bambang Harjito)
283
knowledge representation method can effectively represent the concepts of structure and the
relations between concepts [6]. Ontology languages express a rich semantic and provide best
reasoning capabilities [7]. Building ontology can be a representation of knowledge over
information about the tuberculosis disease. One part of the scientific journal is abstract in form
Indonesian and English language which was used as corpus resource in building ontology.
Besides using corpus, the development of ontology can also use ontology design
patterns (ODPs). Ontology design patterns constituted derivative of the design patterns used in
software engineering. Ontology design patterns were defined as a pattern to identify the
ontology structure design. Design patterns set aside the dependencies between terms so that if
there was a change in the terms, it would not affect the other terms [7]. The use of Ontology
Design Patterns (ODPs) has been shown to have beneficial effects on the quality of developed
ontologies, and promises increased interoperability of those same ontologies [8]. Ontology
design patterns (ODPs) are a proposed solution to facilitate ontology development, and to help
users avoid some of the most frequent modeling mistakes [9]. The ontology design patterns also
offer advantages enabling a more modular, well-founded and richer representation of the
knowledge. This representation will produce a more efficient knowledge management in the
long term [10].
Based on this background, it is important to do research related to the development of
ontology domain bilingual corpus of scientific journals and ontology design patterns to represent
knowledge [11]. The manual construction of ontology that had been done before was too
complex, requiring a lot of time and effort [12]. Therefore, an automatic process is needed to
facilitate the development of ontology. The existing approaches of automatic processes is
ontology design patterns (ODPs) [13].
The main contribution of this paper is present ontology development with automatic
approach using ontology design patterns (ODPs) for tuberculosis domain. This paper improves
the results of research previously. Drame, et al., proposed to develop a semi-automatic ontology
building in Alzheimer domain using corpus and bilingual UMLs Meta thesaurus [14]. Validation
of the ontology used by the expert was to ensure the knowledge in ontology. However, it took
about one month to validate the ontology. Therefore, this paper was developed using the corpus
and ODPs, so that validation can be done without expert. Dahab, et al., [15] build automatic
construction ontology from natural language text using semantic pattern approach. Then, Navigli
and Velardi [16] developed a methodology for automatic ontology enrichment and document
annotation. Natural language definitions from available glossaries were processed and regular
expressions are applied to build the ontology. This paper was different from their studies [14,
15] because it used bilingual corpus and ontology design patterns (ODPs) approach for building
ontology automatically. Mortensen, et al., proposed applications of ontology design patterns
(ODPs) in Biomedical Ontologies [9] and Cahyani, et al., [17] also purposed development
ontology using ontology design patterns (ODPs) in Alzheimer domain, but in this paper we show
the utilization of ODPs to bulid ontology automatically in tuberculosis disease.
2. Resources Used
The resource was divided into data and tools to process terminological resources.
2.1. Data
2.1.1. Corpus
The corpus used in this research was the abstract (English and Indonesia language) in
group health scientific journals in websites such as Health Science Journals, Health Science
Journal of Indonesia, Makara Journal of Health Research. Corpus abstract of a scientific paper
from the journal had enriched knowledge about Tuberculosis. Currently, there were 55 papers
published in this scientific journal and reviewed by expert research domain.
2.1.2. Tuberculosis Glossary
This research used a glossary term to filter the results of a Tuberculosis extraction from
the corpus. The filtering term extraction results were needed to get a term linked to the
Tuberculosis disease. Tuberculosis's glossary obtained at the website address
(http://www.tbindonesia.or.id/). The total of terms which were related to Tuberculosis disease in
this glossary was 840 terms.

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2.1.3. Ontology Design Patterns (ODPs)
Ontology Design Patterns (ODPs) could be accessed at
http://www.gong.manchester.ac.uk/odp/html/index.html. This website also contained a catalog
of ODPs. In this catalog, there were three types of ODPs; (i) Domain Modeling ODPs, (ii) Good
Practice ODPs, and (iii) Extension ODPs. The total number of ontology design patterns in the
catalog was 17 ODPs. ODPs Domain Modeling aimed to get the best model for specific domain
ontology, e.g. Interactor_Role_Interaction and Sequence. Good Practice design pattern
ontology aimed to be better and stronger to maintain ontology models, e.g. Normalization and
Upper-Level Ontology. On the other hand, Extension design pattern aimed to overcome the
limitations of existing ontology models to expand or increase coverage of the ontology, e.g.
Nary_Data Type Relationship and Exception.
2.2. Tools
2.2.1. Text2Onto
Text2Onto is a framework of learning ontology which developed to support ontology
construction from textual documents. Text2Onto has been used by Cimiano and Volker [12].
The research used Text2Onto as a framework for ontology learning from textual resources
based on Probabilistic Ontology Model (POM). There were three processes in Text2Onto:
preprocessing, Executing of Algorithms and Combining results. During preprocessing,
Text2Onto called GATE application to tokenize document and tag Part of Speech sentences to
create indexes for the document, and the result of this process was obtained as an annotation
document. Executing of Algorithms was the process of Text2Onto executed the applied
algorithms to extract terms and relations. One of the applied algorithms was TFIDF Concept
Extraction. The last process was combining results; this process combined the result of
extracted terms and relations derived from processed documents. Text2Onto was available at
http://code.google.com/p/text2onto/downloads/list.
2.2.2. SimMetrics
SimMetrics is an open-source library available in Java, which contains more than 20
similarity distance algorithm, e.g. Jaro-Winkler, Levenstein distance, and Monge-Elkan distance.
SimMetrics used for string correspond to identify the position of string or set of strings within a
text. String correspond algorithms compared two different strings and found the similarity score
between two text comparisons. SimMetrics has been used by Chapman, et al., [18]. This
research used SimMetrics to calculate the similarity between texts, where the information in this
text had been integrated into large repositories (e.g. the Web). SimMetrics was available at
https://github.com/Simmetrics/simmetrics.
2.2.3. Ontology Generation
Ontology generation is a plug-in protégé to build ontology with generating terms of
natural language text. Ontology generation was developed by Watcher and Schroeder,
2010 [19]. This tool supported the creation and extension of OBO ontology by semi-
automatically generating terms, definitions, and parent-child relations from the text in PubMed;
the web, and PDF repositories. This tool generated term by identifying significant noun phrases
in text statistically and for the definitions and parent-child relations, it employed pattern-based
web searches. Ontology generation was available at
http://protegewiki.stanford.edu/wiki/Ontology_Generation_Plugin_(DOG4DAG). Ontology
generation can be applied to the protégé-OWL version 4.3.
3. Research Method
The methods in this research consist of six stages: (a) Term and relation extraction (b)
Matching with Tuberculosis glossary (c) Matching with ontology design patterns (ODPs) (d)
Score computation similarity term and relations with ODPs (e) Ontology building (f) Ontology
evaluation. The process of each stage in the method in this research was in Figure 1.

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Extract Term
Match concepts to
concepts in pattern
Extract relation
Match extracted
relations to relations
in pattern
Compute score
similarity concepts &
relation with pattern
Pattern
Catalogue
Accept patterns
above certain score
Ontology building with
accepted patterns
Bilingual ontology
Evaluation
Term List
Set of matched terms
Relation list
Amount of relations matched
Score for each pattern
Accepted patterns
Discard patterns
Match extracted
concepts to
Tuberculosis
glossary
(a) Term &
Relation extraction
(a) Term &
Relation extraction
(b) Matching with
Tuberculosis
Glossary
(c) Matching with ODPs
(c) Matching with
ODPs
(d) Score Computation
(e) Ontology Building
(f) Ontology Evaluation
Indonesia &
English Corpus
Figure 1. Overview of ontology building method
The main idea of this research was to extract terms and associations and then
Correspond it on design patterns. Then build the ontology and enrich them with parallel corpus.
The stages of the method in this paper were explained as follows.
a) Term & relation extraction
Corpus of Health Science Journals in English and Indonesia were extracted to retrieve
a number of the terms. Next, the extraction of relationships linked between terms to retrieve a
number of relations. This corpus extraction used was Text2Onto.
b) Matching with tuberculosis glossary
After obtaining a number of terms and relations, the next stage was matching with the
glossary of Tuberculosis. At this stage, the matching of Tuberculosis glossary aimed to filter
terms in order to derive from the extracted word list and in the glossary.
c) Matching with ontology design patterns
The extracted terms and relations compared with terms and relations contained in
Catalog ODPs that consist 17 design patterns. The matching result was calculated to get the
score of similarity by using SimMetrics tools that used Euclidean Distance algorithm. Then, two
scores obtained from correspond concepts and relations were weighted together to form a
“total-matching-score” for each pattern. Then a decision was made according to some threshold
value, the patterns were kept and included in the ontology result, which would be discarded.
Finally, an ontology was built from the accepted patterns which have the highest score of
similarity.
d) Score Computation
At this stage, the similarity calculation computed between the extracted term and
relation of the concepts and the relationships that exist in the design pattern. The tool used was
SimMetrics which consisted of various algorithms, e.g. Euclidean Distance similarity distance,

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Levenshtein, and so on. So, average values were calculated from all the existing algorithms, to
obtain value or score for string matching. The result was the value or similarity score for each
design pattern. Afterward, a design pattern that has the highest similarity score was
implemented to build ontology. More attention was given for relation between the concepts
because it was capable of making more structural ontology.
e) Ontology Building
Ontology building was the stage to build ontology of terms and relations that correspond
to ontology design patterns. The ontology constructed implement design pattern that has the
highest similarity values on the ontology of Tuberculosis that was built. This stage used OWL
ontology generation to build ontology from terms and relationships that exist. The first step to
use ontology generation was search definition of the term entered. The search was connecting
with PubMed in the protégé. Then, the automatic map of terms and relation existed as to build a
new ontology.
f) Ontology Evaluation
Ontology evaluation was viewed in terms of complexity, time and effort required to build
this ontology. Moreover, ontology evaluation also calculated the accuracy of the terms and
relation that used to build the ontology. Accuracy is calculated by the following formula:
(1)
x = matching results of term/relation
y = total all of match term/relation
x was the matching results of term or relation suitable terms and relations extracted
from the corpus and in design patterns that have the highest score similarity.
Meanwhile, y was the total all term/relation extracted from the corpus and had been
filtered by Tuberculosis’s Glossary. Those terms and relations were corresponded with the
terms and relation on ODPs.
4. Result and Analysis
This section is about the results of the steps for building a fully automatic ontology
construction.
a) Term & relation extraction
The results at this stage were a collection of terms and relationships from corpus
extracted by using Text2Onto. The corpus used in this study was 55 papers. The results
obtained were 1310 terms and 44 relations between terms. The number of the terms resulting
from the extraction of corpus turns out quite a lot, so it was needed to be filtered to get the
appropriate terms that related to the Tuberculosis disease.
b) Matching with Tuberculosis Glossaries
The result of terms and relations extraction of this stage was filtering by matching
Tuberculosis’s glossary contained 860 terms related to Tuberculosis which acquired 260
matching terms. This was different from the terms extracted from a corpus using extraction with
Text2Onto because the extraction term related many health terms in general, not specifically
related to Tuberculosis disease. In addition, the number of terms in the Tuberculosis’s was less
than terms of general health glossary so the scope of term filtering would be limited.
c) Matching with ontology design patterns
Terms and relations that had been filtered would be corresponded with a list of terms
and relations that exist in the ontology design patterns. In the catalog, there were several kinds
of ontology design patterns (ODPs). The corresponded results were calculated for the similarity
values between terms and filtered results with a term relation and relation that exist in the
ontology design patterns (ODPs).
d) Score computation
The result of similarity matching between term and relation with each ontology design
patterns are shown in Table 1.
The highest value of similarity found in ontology design patterns closure was equal to
81%. Closure ontology design pattern was a design pattern that limits the relationships among
concepts which allowed it to happen by clarifying the relation [20]. The limitations in this relation

287
were to express a concept has had a particular relation and only those relations, e.g. a
carnivorous is a meat eating animals, with closure design pattern was revealed that carnivores
do not eat other foods besides meat.
Table 1. Result of the Similarity Calculation ODPs
No ODPs Type Name Similarity score
1 Domain_
Modelling_ODP
Adapted_SEP 80%
2 CompositePropertyChain 80%
3 Interactor_Role_
Interaction
79%
4 List 76%
5 Sequence 78%
6 Extension_ODP Exception 80%
7 Nary_DataType_
Relationship
80%
8 Nary_Relationship 79%
9 Good_practice Closure 81%
10 DefinedClass_
Description
80%
11 Entity_Feature_
Value
76%
12 Entity_Property_
Quality
80%
13 Entity_Quality 80%
14 Normalisation 80%
15 Selector 79%
16 Upper_Level_
Ontology
78%
17 Value_Partition 80%
e) Ontology Building
The ontology built in this research consisted of several components; there were 362
terms and 44 relations. Terms and relations used to build the ontology was OWL ontology
generation. There were 260 new terms added in that ontology. Figure 2 represented the results
of the ontology that has been built in the protégé editor tool.
Figure 2. Visualization a part of the ontology in protégé

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f) Ontology Evaluation
The result of accuracy value of fully automatic ontology construction was 71%. It was
obtained from the calculation of matching number of 260 terms or relations and the total term or
relation in the ontology built 362 terms or relations. This indicated that fully automatic ontology
construction method used in the study was quite excellent to be able to build the ontology. The
results of accuracy in this study were similar to those of previous studies [17] in the domain of
Alzheimer's disease that resulted in 71% accuracy. The accuracy value can be as a supporting
material to the evaluation of this research.This indicates the method of ontology construction
that use Ontology Design Patterns (ODPs) in this research can be applied to various domains
and get good accuracy value on the result.
The evaluation of ontology that has been built can be seen in terms of the complexity,
time and effort required. The results of automatic ontology construction were able to shorten the
time when compared to the construction of ontology manually or semi-automatic that required
validation of at least one-month length of an expert [14]. In this study we just need several days
to validation the ontology with Ontology Design Patterns (ODPs). This indicated the method in
this paper can save time to building the ontology. In previous studies [14] it takes two teams
in the field of Alzheimer's expert to validate the built of ontology. While in this study does
not require expert to validate ontology so we can save effort to build ontology automatically. So
in this paper, we have the advantage of time and effort required aspect to build ontology
construction.
5. Conclusion and Future Work
This research succeeded to make fully automatic bilingual domain ontology using the
Ontology Design Patterns (ODPs) and corpus. The result of ontology development included 361
terms and 44 relations with the addition of 260 terms. The calculation accuracy of ontology
construction was 71%.Fully automatic construction could speed up and decrease the human's
role as the expert to evaluate ontology rather than building ontology manually. The result of the
evaluation was fully automatic ontology constructions that shorten development time compared
to manual ontology or semi-automatic which required expert validation.
For future work, it is suggested to add more terms and relation in Tuberculosis’s
glossary in order to have well filtered terms results of corpus extraction. In addition, type of data
ontology design patterns (ODPs) can be improved to get the highest similarity value for selected
design patterns that implemented to build the ontology. Moreover, ontology enriches the number
of terms in order to be implemented in ontology building. Ontology enrichment using parallel
corpus of the website in English and Indonesia can obtain terms and synonymous terms in other
languages.
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