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Knowl. Org. 37(2010)No.3 173
S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
A Method for Developing a Domain Ontology:
A Case Study for a Multidisciplinary Subject†
Sangeeta Deokattey,* Arashanipalai Neelameghan,** and Vijai Kumar***
*Scientific Information Resource Divn., Bhabha Atomic Research Centre,
Trombay, Mumbai-400 085,
**Documentation Research and Training Centre, Indian Statistical Institute,
Bangalore-560 059,
***Knowledge Management Group, Scientific Information Resource Divn.,
Bhabha Atomic Research Centre, Trombay, Mumbai-400 085,
Sangeeta Deokattey has been working in the field of library-and-information science for the last 25
years. She developed and organized the Scientific and Technical Reports collection at the Small Enter-
prises National Documentation Centre. Later, at the Research Centre for Women’s Studies, SNDT
Women’s University, she organized the special collection of Women’s Studies research material and
was one of the first in Mumbai to develop a database on Women’s Studies. Her research interests in-
clude classification, indexing, and bibliometric analysis. She is presently working as a Senior Scientific
Officer in the Scientific Information Resource Division, Bhabha Atomic Research Centre, India.
Arashanipalai Neelameghan retired as UNESCO/PGI Regional Adviser for Asia-Pacific. Former Pro-
fessor and Head of ISI/DRTC, Bangalore. Special interests: knowledge organization (KO); KO tools;
relationship among concepts; information communication with marginalized communities; education
and training of information professionals.
Vijai Kumar is Associate Director, Knowledge Management Group and Head, Scientific Information
Resource Division, Bhabha Atomic Research Centre, India. He is currently involved in executing a
plan project for upgrading scientific and technical information resources, including setting up of a
modern digital library. He is also a Senior Professor at the Homi Bhabha National Institute (a deemed
University in Mumbai) and is associated with academic and scientific productivity analysis.
Deokattey, Sangeeta, Neelameghan, Arashanipalai, and Kumar, Vijai. A Method for Developing a
Domain Ontology: A Case Study for a Multidisciplinary Subject. Knowledge Organization, 37(3),
173-184. 11 references.
ABSTRACT: A method to develop a prototype domain ontology has been described. The domain se-
lected for the study is Accelerator Driven Systems. This is a multidisciplinary and interdisciplinary
subject comprising Nuclear Physics, Nuclear and Reactor Engineering, Reactor Fuels and Radioactive
Waste Management. Since Accelerator Driven Systems is a vast topic, select areas in it were singled out
for the study. Both qualitative and quantitative methods such as Content analysis, Facet analysis and
Clustering were used, to develop the web-based model.
†
The authors are grateful to Dr. Pitamber Singh and Dr. P.K. Nema, Nuclear Physics Division, BARC, Dr. S.B. Degweker,
Theoretical Physics Division, BARC, Dr. R.C. Sethi (Retd.) Accelerator and Pulse Power Division, BARC for their expert
comments, suggestions and advice.
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
1.0 Introduction tools to develop new ontologies. Ontolingua and
Protégé are among the popular tools in use today. In
Ontologies as subjects of study cut across the do- the field of information science, research and devel-
mains of Philosophy, Logic, Artificial Intelligence, the opment work on ontologies began around late 1990s.
Internet and Information Science. One of the earliest The Unified Medical Language System (UMLS)
reported associations between ontologies and Infor- Metathesaurus (umls.info.nlm.nih.gov) is one of the
mation Science, was made by Dahlberg (1978). She es- most comprehensive domain ontologies in the field
tablished the link between cognitive processes, objec- of biomedical information. Ding (2001) reviewed the
tive knowledge, concepts and their relations, and laid importance of ontologies in the development of the
the ontological foundations of a modern classification Semantic Web. He discussed the definition of on-
system. The relationship between classification theory, tologies, kinds of ontologies, ontology tools, ontol-
cognitive science and artificial intelligence was demon- ogy language and a few ongoing ontology projects.
strated by Walker (1981) (Table 1). A modified ver- Ding and Foo (2002) in another study, presented a
sion of the table displays how this relationship can be two-part review. In the first part of the review, state-
extended to ontologies too and shows the underlying of-the-art techniques on semi-automatic and auto-
unifying basis of all the four domains. matic ontology generation were detailed. The second
Both conventional knowledge organization tools part of the review dealt with ontology mapping and
such as classification schemes, thesauri and new evolving. One of the first experiments to develop an
tools (Denda 2005) such as ontologies, define con- ontology using a traditional knowledge organization
cepts and relationships in a systematic manner. The- tool was reported by Qin and Paling (2002). They
sauri (Lopez-Huertas 1997) and domain ontologies used the controlled vocabulary of Education Re-
are both representational vocabularies restricted to a sources Information Centre (ERIC) descriptors, to
particular domain. The basic difference between develop an ontology in the field of education, using
them, lies in the use of descriptors and concepts to Ontolingua. According to them, the major differ-
map a given domain. A few other differences have ence between a thesaurus and an ontology, lies in the
been elaborated in Table 2. values added through deeper semantics in describing
digital objects, both conceptually and relationally.
2.0 Current approaches for developing
domain ontologies 3.0 The present study
Many of the current methods for building, merging This study uses a web-based approach to represent
and reusing ontologies have been developed by arti- the ontology in the selected domain. For the purpose
ficial intelligence researchers. Most of the current of the study, the following working definition of a
ontological projects use readily-available software domain ontology was adopted:
Cognitive Psychology Classification Artificial Intelligence Ontologies*
Thinking Semantic linkages of ele- Heuristics and Identifying concepts and
ments Predicate Calculus vocabulary of a domain
Perception Syntactic (metadata) Production and Developing the ontology
Structure of the Management Systems language
System and its rules
Remembering Thematic relationships Semantic Networks Conceptualization
(Hierarchical or of the domain
Associative)
Imagination and Creativity Facets of the corresponding Frames Reusable modules of ontolo-
elements of a Subject (based gies based on specific user
on Literary Warrant) requirements
Table 1. Relationship between Classification theory, Cognitive Science, Artificial Intelligence and Ontologies. Source:
Walker (1981) (*the table has been modified through the addition of the column on ontologies).
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Thesaurus Ontology
Rigid Totally flexible
Unidimensional Multidimensional
A thesaurus can either be manually created or ma- An ontology can only be created using special software pro-
chine-generated grammes.
Uses a controlled vocabulary, consisting of descrip- Uses both free index terms as well as a controlled vocabulary in the
tors which represent the subject domain form of concepts. A set of inference rules are also incorporated in
an ontology that aid a user in discovering new knowledge
Controlled vocabulary terms are mostly restricted to An ontology incorporates both domain
domain knowledge knowledge and operational knowledge
Built on the concept of Literary Warrant. Termino- Literary Warrant as well as other problem solving practical tools,
logical concepts and their relationships (including devices and elements. Terminological concepts as well as other
incorporation of new terms) are purely based on Lit- characteristics and properties are defined in the purpose of the on-
erary Warrant. tology and may be shifted to new slots, or deleted from existing
slots, depending on emerging applications.
Only valid descriptors are used both during storage Though a vocabulary is an inherent part of an ontology, other con-
as well as retrieval cepts and terms are freely used, both during storage and retrieval,
as well as for other applications
There are only three kinds of relationships between The relationships between classes or properties are potentially
any two descriptors in a thesaurus; Hierarchical, polyhierarchical; a class may be a subclass of one, two, three or
Equivalence and Associative more superordinate classes
The superordinate and subordinate classes are de- The superordination and the subordination of classes is mostly de-
termined solely by the domain cided by the ontology developer depending on the purpose of the
ontology.
Descriptors in a thesaurus exist purely at a concep- Terms or classes in an ontology can be assigned values.
tual level.
Table 2. A few differences between a thesaurus and a domain ontology
A domain ontology is a knowledge organization driven system is a hybrid system. It combines a parti-
tool on a specific subject domain either pure, in- cle accelerator, particularly a high power proton accel-
terdisciplinary or multidisciplinary and which in- erator with a subcritical nuclear reactor to accomplish
corporates both a metadata schema and a vo- various tasks. Research and development work on ac-
cabulary. It is based on WWW standard for celerator driven systems gains importance due to their
metadata encoding. It uses both available con- inherent safety features, their use of thorium as fuel,
trolled vocabulary terms and free index terms and their non-proliferative advantage (Carminati et al
and is based on the concept of literary warrant. 1993; Gokhale et al 2006). In this study, a domain on-
tology was developed on accelerator driven systems.
3.1 The multidisciplinary subject domain: Though there are several applications of accelerator
accelerator-driven-systems (ADSs) driven systems, the study was restricted to informa-
tion on a) accelerator driven systems in general, b) ac-
Accelerator-driven-systems is a narrow subject do- celerator driven systems for energy production (en-
main, a part of the field of nuclear science and tech- ergy amplifier (EA)) and c) accelerator driven systems
nology. The domain of nuclear science and technology for transmutation of waste (accelerator transmutation
is itself multidisciplinary, as it encompasses physics, of waste (ATW)) applications.
chemistry, engineering and biology disciplines. It is
also interdisciplinary as it is an amalgamation of all 3.2 The method used for the present study
these domains. Accelerator-driven-systems is a part of
the broad area of physics, especially particle physics There are three methodological options (Mai Chan
and atomic and nuclear physics. It also includes nu- and Lei-Zeng 2002) for developing new systems or
clear and reactor engineering, reactor fuels and radio- tools for knowledge organization purposes, espe-
active waste management. Basically, an accelerator cially in networked environments:
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
– translation, i.e., to integrate one or more existing #13 ADSS (Accelerator Driven Subcritical
tools or systems to create more powerful ones; Systems) and accelerator
– merging, i.e., mapping of a vocabulary from a lar- #12 accelerator driven subcritical reactor
ger system and developing a new one or mapping #11 hybrid reactor and accelerator
two or more existing schemes; #10 thorium breeding
– creating from scratch, i.e., developing an entirely #9 energy amplifier and accelerator
new system. #8 advanced accelerator applications
#7 AAA (Advanced Accelerator Applica-
In this case study, the mapping/merging system was tions) and accelerator
used and a new knowledge organization tool was de- #6 accelerator driven transmutation tech-
veloped using an existing larger tool; the Interna- nolog*
tional Nuclear Information System (INIS) thesau- #5 accelerator driven energy production
rus. This is more economical and it also ensures in- #4 accelerator driven systems
teroperability, since the INIS thesaurus is a standard #3 ADS (Accelerator Driven System) and
vocabulary control tool, in the field of nuclear sci- accelerator
ence and technology. #2 (accelerator driven*) in DE
Appropriate bibliographic searches conducted on #1 accelerator transmutation of waste
the INIS database (under the guidance of subject ex- Figure 1. Query formulation used for searching the INIS
perts), yielded a total of 2,238 bibliographic records database
on ADSs in general: 694 records on ATW and 129
records on EA. Figure 1 shows the query formula- The following steps formed part of the method:
tion. All the descriptors (both computer-assigned
and indexer-assigned) from each bibliographic re- 1. Downloading of the assigned descriptors (a total of
cord were downloaded separately. Free-text key- 4808) and identification of new free-text keywords
words were also selected from the abstracts of the on ADSs (a total of 474), from the abstracts of the
downloaded records. Using the basic principles of downloaded records, using content analysis.
content analysis, facet analysis and clustering, an on- 2. Numerical listing of the descriptors and free-text
tological framework for ADSs was developed. keywords on ADSs, based on frequency count
and selection of those with a threshold level of
#20 (accelerator driven energy production) or more than 2. A total of 3085 descriptors and all
(energy amplifier and accelerator) or the 474 keywords were finalized.
(ADEP and accelerator) 3. Developing a semantic network of selected de-
#19 ((accelerator driven*) in DE) or (ADS scriptors and keywords on ADSs through the
and accelerator) or (accelerator driven creation of Excel files. All the 3085 descriptors
systems) or (AAA and accelerator) or and the 474 keywords were linked to each other
(advanced accelerator applications) or through subordinate, superordinate and affinitive
(hybrid reactor and accelerator) or (ac- relationships. Three separate files were developed
celerator driven subcritical reactor) or on ADSs, ADT and EA subfields. A sample of
(ADSS and accelerator) or (ADSR and one of the Excel files is shown in Figure 2.
accelerator) 4. Developing clusters on the basis of frequency
#18 (accelerator transmutation of waste) or count of the descriptors. Descriptors with very
(accelerator driven transmutation tech- high frequency counts were identified as core de-
nolog*) or (ADTT and accelerator) or scriptors or concepts and clusters were developed
(ATW and accelerator) around them, through the generation of one-to-
#17 ATW (Accelerator Transmutation of many correspondences, using facet analysis. (For
Waste) and accelerator example, for the descriptor ‘targets,’ which forms
#16 ADEP (Accelerator Driven Energy Pro- part of the larger cluster ‘accelerators,’ five facets
duction) and accelerator were identified: type of targets; purpose or appli-
#15 ADTT (Accelerator Driven Transmuta- cation of the target; parts of the target; specific
tion Technologies) and accelerator targets; and material of the target. The material
#14 ADSR (Accelerator Driven Subcritical facet was again subdivided into two parts: solid
Reactor) and accelerator material such as metal and liquid material such as a
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Descriptor/Keyword Superordinate Subordinate Affinitive
heterogeneous-effects; radiations;
self-shielding; shielding; slowing-
absorption-; down;stopping-power; transmission
absorption-spectroscopy; spectroscopy- infrared-spectra;
abstracts-; document-types; leading-abstract;
acceleration-; accelerators-; velocity
accelerators-; breeder-reactors;
breeding; fissionable-
accelerator-breeders; materials;nuclear-fuels
accelerator-driven- accelerator-breeders;accelerators; ra-
transmutation; transmutation- dioactive-waste-processing
accelerators-; beam-dumps; beam-
monitors; laboratory-equipment; re-
accelerator-facilities; target-chambers action-product-transport-systems
coherent-accelerators; collec-
tive-accelerators; cyclic-
accelerators; electrostatic- acceleration; accelerator-breeders;
accelerators- accelerators; heavy-ion- accelerator-driven-transmutation;
accelerators; linear- accelerator-facilities; beam-dumps;
accelerators; meson-factories; beam-dynamics; beam-separators;
particle-beam-fusion- impact-fusion-drivers; isotope-
accelerator production; particle-boosters; stora-
ge-rings; targets; target-chambers
accident-insurance; insurance-; accidents-;
Figure 2. A sample of the interlinked EXCEL file of keywords / descriptors on Accelerator Driven Systems
liquid combination of lead and bismuth at high Research programs: coordinated-research-pro-
temperatures). The highest ranking core descrip- grams
tors were: fuels, transmutation, accelerators, reac- International organizations: national organiza-
tors, elements, materials, nuclear-reactions, sub- tions, French organizations, Japanese organiza-
critical-assemblies, and management. A total of tions.
nine separate Excel files on all these nine core de- 5. Uploading the above semantic network onto a
scriptors were developed. Thus the final set of 12 three tier, web-based architecture of active server
interlinked Excel files formed a semantic network pages, Web server (IIS), and MS Access.
of the entire field of accelerator-driven-systems. 6. Saving the other bibliographic details of the
Certain descriptors could not be slotted into any downloaded records on ADSs, such as author, ti-
of the nine major clusters and these were just tle etc. as html files and uploading them onto the
listed alphabetically. For example: web-based platform.
Data: data-covariances, evaluated-data, experi- 7. Developing a home page and a user interface for
mental-data, theoretical-data the prototype domain ontology on ADSs, with
Calculation methods: composite-models, mathe- security provisions.
matical-models, Monte-Carlo-method, numerical- 8. Developing separate hyperlinks to the three select
analysis, simulation areas of ADSs on the home page.
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Figure 3. Prototype model of the domain ontology on Accelerator Driven Systems: a graphical representation
9. Testing and validation of the prototype model by a particular alphabet, displays all the keywords/con-
experts on ADSs. (A prototype model of the do- cepts under that alphabet. There is a provision to add
main ontology can be seen in Figure 3). even full-text information on ADSs to the domain
ontology and make it searchable.
3.3 The domain ontology on ADSs The URL of the domain ontology is http://brew-
as a knowledge organization tool 2006.com/barc. After typing in the LOGIN ID and
PASSWORD, the homepage (Figure 4) is displayed.
The author, title, abstract and keywords/concepts Figure 5 illustrates the term “ACCELERATOR DE-
fields were made searchable. A user can search this SIGN CONCEPTS.” This term, suggested by ADS
domain ontology through the use of alphabetical A- experts, brings all the descriptors and keywords under
Z drop-down menu of keywords/concepts; selecting one umbrella. The user can see at a glance, all the re-
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Figure 4. Homepage of the domain ontology on ADS
lated keywords associated with it and select any or keywords selected from the abstracts, ADS experts
all of the keywords to include them in his/her suggested the addition of the following set of twenty
search. The “advanced search options” can be used to one new terms, which they thought were important.
conduct searches other than on subject. Apart from These were also incorporated into the domain ontol-
the assigned descriptors and the additional free text ogy.
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Figure 5 (continued on next page)
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Figure 5 (continued on next page)
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
Figure 5. A display of the term “ACCELERATOR DRIVEN CONCEPTS”
1. Medium Energy Beam Transport (MEBT) 8. Postcouplers
2. Quality factor 9. Dipole stabilizer rods
3. Power coupler 10. Beam diagnostics
4. Twiss parameter 11. FOcusing and DefOcusing (FODO)
5. Waveguides 12. FOcusing- FOcusing and DefOcusing-
6. Permanent magnet quadrupole DefOcusing (FOFODODO)
7. Tuners 13. Separated Drift Tube Linac (STDL)
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S. Deokattey, A. Neelameghan, and V. Kumar. A Method for Developing a Domain Ontology
14. Coupled Cavity Linac (CCL) can also be saved as XML files. It is easy to update as it
15. Shunt impedance uses terms both from a standard vocabulary as well as
16. Quadrupole gradient free text terms and is therefore very flexible. A flat
17. Phase advance structure for a domain ontology as displayed in Figure
18. Electron trap 4 was found to be more appropriate for an interdisci-
19. Coupling cells plinary subject domain such as accelerator-driven-
20. End cells and systems, instead of the traditional “top down” or
21. Circulators. “bottom up” approach. The same structure can be ap-
plied to any other interdisciplinary and multidiscipli-
3.4 Maintaining and updating the prototype domain nary subject domain. (The authors conducted a small
ontology on ADSs pilot study on the interdisciplinary domain of bio-
technology, using the same method). A domain on-
Accelerator-driven-systems is an emerging and wi- tology enriches the quality of semantic information in
dely researched subject domain. New keywords, any given domain due to the various linkages provided
which would be an integral part of the growing vo- between the keywords, through the identification and
cabulary on ADSs can be added to the domain on- representation of one-to-many correspondences. These
tology. New linkages can also be added to the exist- correspondences in the form of lateral or RT relation-
ing keywords or they could be modified altogether. ships have to be precisely identified and appropriately
This is an ongoing process and can be done under represented. In the present study, the identification of
expert advice and guidance. these relationships was done under the guidance of
subject experts; the representation of these relation-
4.0 Conclusions ships was achieved through facet analysis and the type
of relationship existing between any two keywords or
Conceptualization is at the core of a domain ontology. group of keywords, was exactly specified to aid both
To identify concepts in a narrow domain, (the domain the novice user as well as to save the time of the sub-
of ADSs itself is comparatively new (early 1990s)) de- ject expert. Clustering of descriptors into concepts
scriptors in a thesaurus can be used as basic building (on the basis of users’ requirements) was seen to en-
blocks and to harness these descriptors, appropriate hance retrieval of relevant information and was found
query formulation is essential. Thus in the present to be a useful method for more effective knowledge
study, descriptors from the INIS thesaurus were used, organization in interdisciplinary and multidisciplinary
to organize the growing and vast amount of informa- subject domains.
tion on ADSs and a suitable knowledge organization
tool was developed. International databases either bib- References
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