Hi Marc, check:
www.biomedcentral.com/content/pdf/1471-2105-7-267.pdf (01)
Chronologically, early proposed methodologies such as Methontology,
Usher's, etc were mostly considering static scenarios for which the
ontology could be developed and deployed on a one off basis;
furthermore, these methodologies were mostly designed for development
scenarios for which domain experts were not leading the process and
could be gathered in one place; geographically decentralized settings
were not initially considered. As ontologies became a cornerstone for
the Semantic Web, the networked feature of the models gained
prominence, so did communities as primary developers and consumers of
technology using ontologies. Consequently methodologies started to
address these issues; for instance Pinto et al, [8] as well as Garcia
et al [9] explicitly addressed the problem of developing ontologies
within decentralized settings. Important issues such as the evolution,
maintenance, intrinsic networked nature, modularization, reusability
and life cycle of the ontologies have also started to be more
carefully considered within recent methodological frameworks [5, 10]. (02)
From espace.library.uq.edu.au/view/UQ:158766 u will be able to find
two particularly interesting chapters. the first one is a review, the
other one is a methodology that reuses a lot from all of previously
proposed methodologies. (03)
A quick sumary for developing ontologies: (04)
Step 1: The first step involves addressing straightforward questions
such as: what is the ontology going to be used for? How is the
ontology ultimately going to be used by the software implementation?
What do we want the ontology to be aware of, and what is the scope of
the knowledge we want to have in the ontology? (05)
Step 2: When identifying reusable ontologies, it is important to focus
on what any particular concept is used for, how it impacts on and
relates to other concepts, how it is embedded within the process to
which it is relevant, and how domain experts understand it. It is not
important to identify exact linguistic matches. By recyclability of
different ontologies, we do not imply that we can indicate which other
ontology should be used in a particular area or problem; instead, we
mean conceptually how and when one can extrapolate from one context to
another. Extrapolating from one context to another largely depends on
the agreement of the community, and specific conditions of the
contexts involved. Indicating where another ontology should be used to
harmonise the representation at hand is a different issue that we
refer to as reusability. (06)
Step 3: Domain analysis and knowledge acquisition are processes by
which the information used in a particular domain is identified,
captured and organised for the purpose of making it available in an
ontology. This step may be seen as the ?art of questioning?, since
ultimately all relevant knowledge is either directly or indirectly in
the heads of domain experts. This step involves the definition of the
terminology, i.e. the linguistic phase. This starts by the
identification of those reusable ontologies and terminates with the
baseline ontology, i.e. a draft version containing few but seminal
elements of an ontology. We have found it important to maintain the
following criteria during knowledge acquisition: (07)
? Accuracy in the definition of terms. The linguistic part of our
development was also meant to support the sharing of
information/knowledge. The availability of context as part of the
definition proved to be useful when sharing knowledge. (08)
? Coherence: as CMs were being enriched it was important to ensure the
coherence of the story we were capturing. Domain experts were asked to
use the CMs as a means to tell a story; consistency within the
narration was therefore crucial. (09)
? Extensibility: Our approach may be seen as an aggregation problem;
CMs were constantly gaining information, which was always part of a
bigger narration. Extending the conceptual model was not only about
adding more details to the existing CMs, nor was it just about
generating new CMs; it was also about grouping concepts into
higher-level abstractions and validating these with domain experts.
Scaling the models involved the participation of both domain experts
and the knowledge engineer. This was mostly done by direct interview
and confrontation with the models from different perspectives. The
participation of new ?fresh? domain experts as well as the
intervention of experts from allied domains allowed us to analyse the
models from different angles. This participatory process allowed us to
re-factorise the models by increasing the level of abstraction. (010)
The goal determines the complexity of the process. Creating an
ontology intended only to provide a basic understanding of a domain
may require less effort than creating one intended to support formal
logical arguments and proofs in a domain. We must answer questions
such as: Why are we building this ontology? What do we want to use it
for? How is it going to be used by the software layer? Subsections
Identification of purpose, scope, competency questions and scenarios
to Iterative building of informal ontology models explain these steps
in detail. (011)
Step 4: Iterative building of informal ontology models helped to
expand our glossary of terms, relations, their definition or meaning,
and additional information such as examples to clarify the meaning
where appropriate. Different models were built and validated with the
domain experts. (012)
Step 5: Formalisation of the ontology was the step during which the
classes were constrained, and instances were attached to their
corresponding classes. For example: ?a male is constrained to be an
animal with a y-chromosome?. This step involves the use of an ontology
editor. (013)
Step 6: There is no unified framework to evaluate ontologies, and this
remains an active field of research. We consider that ontologies
should be evaluated according to their fitness for purpose, i.e. an
ontology developed for annotation purposes should be evaluated by the
quality of the annotation and the usability of the annotation
software. By the same token, the recall and precision of the data, and
the usability of the conceptual query builder, should form the basis
of the evaluation of an ontology designed to enable data retrieval (014)
if u need any further assistance contact me off line, I can send u
some papers and give u some hints. As ontology editor I use Protege,
my word of advice: use protege 3.4.1. Use Pellet as reasoner, and also
do some research in what has to do with plug ins. Some of them, for
instance DataMaster, will make it easy for u to use a a knowledge
source data base schematas; u get a baseline ontology quite easily.The
problem that I see in most of proposed methodologies is the lack of
detail. Most of them are proposed as empty "things"; u will have to
figure out how to fill them with tools, methods and techniques for
those proposed steps. Also, focus on what is the onto going to be used
for. Cheers. (015)
> marc.l.walker@xxxxxx wrote:
>> Hi all,
>>
>> I was wondering if anyone might be able to help me by sharing some
>> thoughts on and/or experiences of using ontological development
>> methods. I am about to embark on the development of a business
>> related ontology and would like to use an appropriate method to
>> provide credibility and guidance to the development process. Also
>> using a method would help the members of the team who are
>> relatively new to this. The three methods I am considering are:
>> * OTK method - On-to-Knowledge
>> * Methontology
>> * Ushold and King
>>
>> The source for each method comes from the publication by Springer -
>> Ontological Engineering. The ontology will be developed in protégé
>> v4 and will support software developers, vendors and the
>> construction of enterprise knowledge bases. We also expect the
>> ontology to evolve incrementally but I would like to have a
>> recognised method to support the design and review activities.
>>
>> What would be useful would be any comments on the methods - I have
>> not used any of the three - or suggestions to other methods to
>> consider.
>>
>> Thanks in advance
>>
>> Rgds
>> Marc
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>
> --
> --------------------------------------------------------------
> martin hepp
> e-business & web science research group
> universitaet der bundeswehr muenchen
>
> e-mail: hepp@xxxxxxxxxxxxxxxxxxx
> phone: +49-(0)89-6004-4217
> fax: +49-(0)89-6004-4620
> www: http://www.unibw.de/ebusiness/ (group)
> http://www.heppnetz.de/ (personal)
> skype: mfhepp twitter: mfhepp
>
> Check out GoodRelations for E-Commerce on the Web of Linked Data!
> =================================================================
>
> Webcast:
> http://www.heppnetz.de/projects/goodrelations/webcast/
>
> Recipe for Yahoo SearchMonkey:
> http://www.ebusiness-unibw.org/wiki/GoodRelations_and_Yahoo_SearchMonkey
>
> Talk at the Semantic Technology Conference 2009: "Semantic Web-based
> E-Commerce: The GoodRelations Ontology"
>
>http://www.slideshare.net/mhepp/semantic-webbased-ecommerce-the-goodrelations-ontology-1535287
>
> Overview article on Semantic Universe:
>
>http://www.semanticuniverse.com/articles-semantic-web-based-e-commerce-webmasters-get-ready.html
>
> Project page:
> http://purl.org/goodrelations/
>
> Resources for developers:
> http://www.ebusiness-unibw.org/wiki/GoodRelations
>
> Tutorial materials:
> CEC'09 2009 Tutorial: The Web of Data for E-Commerce: A Hands-on
> Introduction to the GoodRelations Ontology, RDFa, and Yahoo!
> SearchMonkey
>
>http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_IEEE_CEC%2709
>
>
> (016)
Alexander Garcia
http://www.usefilm.com/photographer/75943.html (017)
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