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A Methodology to Develop Ontologies for Emerging Domains
Chai Meenorngwar

Author’s Note
The author would like to thank the Royal Thai Government and Valaya Alongkorn
Rajabhat University.

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Abstract
The characteristic of complex, dynamic domains, such as an emerging domain, is thatthe information necessary to describe them is not fully established. Standards are notyet established for these domains, and hence they are difficult to describe and present,
and methods are needed that will reflect the changes that will occur as the domains
develop and mature.

This research proposes the Liverpool Metadata or LiMe

methodology to develop an ontology and organise the knowledge that is necessary fordeveloping the domain environment descriptions. Its aim is to capture Knowledge
Information (KI) from research articles and translate this into semantic information
with web description languages such as XML(s), RDF(s), and OWL. LiMe represents
an Ontological Framework, which provides the concept characteristics, represented asa concept framework that specifies conceptualisations of the knowledge. LiMe
supports the Semantic Web development. “e-Learning” has been chosen as an
example of an emerging domain in this research. The characteristics of e-Learning
concepts will be extracted from research articles of journal websites such
ScienceDirect, Springer, etc and represented as knowledge. LiMe also explicitly
represents how these concepts are developed and evolve to represent the domain.
Keywords: E-learning domain; emerging domain; knowledge information; ontology;
semantic Web

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Introduction
Overview
The range and quantity of information available via the Internet today has createdwell-known problems of information overload, including difficulty of access andproblems of selecting information that is appropriate and reliable. To address these
problems, ways were required to categorise and organise information for access byusers. The idea of using multiple sources can facilitate the reliability of knowledge,
but increases the need for effective knowledge management.

A domain of knowledge can typically be seen from different perspectives. Also,
information about them is diverse and possibly contradictory. Think for example ofthe huge mass of information contributed every day on the Internet. Therefore,
methods are needed to classify and identify information to find reliable sources toconstruct the knowledge.

In addition, information can change and be flexible, based on time and need. For
example, complex domains such as software development have a lot of platforms andstandards. Knowledge or concepts in the domain have been defined or represented indifferent ways. Therefore, users find it difficult to choose the suitable system orconcepts for their own environmental needs.

This shapes a complex and unstructured environment where unstable concepts andinformation are contributed all the time in a domain. The representation itself of thedomain is also difficult. It needs methods to capture new concepts, organise existing
concepts, and translate into well-formed information that could be shared and reused.

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Objectives
The work in this research sits broadly in the field of Knowledge Management (KM).
KM (Eriksson, H., 2004) is identified as the capabilities and communication that
include: (1) converting individual to group-available knowledge; (2) converting data
to knowledge; (3) converting text to knowledge; (4) connecting people to knowledge;(5) connecting knowledge to knowledge; (6) connecting people to people; and (7)connecting knowledge to people. It is represented as the combination of documents
and ontology that can help organisations describe, store, catalogue, and retrieve
information in a systematic manner.

This research introduces an approach that can help the users to classify theirinformation and to represent it with a well-formed structure. The approach provides
an ontological framework to structure one individual existing domain. This work
focuses in particular on the problem of information management in an organisation.
Information within an organisation needs to be accessed for different purposes.
Experiences from individuals in the organisation help forming the common
understanding, which could used or reused to develop new information, therefore it
needs to be made shareable and reusable. In fact, individual experiences are a very
important source of knowledge. For examples, researchers use the educational
experiences to find the information about their experiences, governors used theworking experiences to organise their daily information, and teachers collected theinformation from books, experiments, and so on to prepare their courses.

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The aim of the research described in this research is to investigate issues involved in
the representation and management of knowledge arising in an emerging domain. A
number of techniques have been used for representing domain knowledge. In mostcases, these methods assume the existence of a well-defined body of knowledge thatcan be assumed to be reliable and definitive, and needs only to be organised
appropriately. In the case of emerging domains of knowledge, however, theseassumptions are not valid. In this case, the “body of knowledge” is incomplete andconstantly changing, and may include significant errors, inconsistencies, andinstances of different assumptions, conclusions and terminology. Only when thedomain reaches a state of relative maturity can these issues be resolved definitively.
Meanwhile, however, there remains a need for researchers and practitioners to makeuse of the knowledge while it is in this state of evolution.

This research proposes a framework, Liverpool Metadata (LiMe), as the way totransform the individual experiences into relevant information for a particular domain
by applying an ontology approach, structuring these experiences in terms of concepts
and the relation between concepts. Concepts are defined from different perspectives
under the same domain. These could be redefined, reused and described as
specification of the particular domain. The development processes of the LiMe
methodology is described in the following section. LiMe provides techniques tomeasure the relation between the concepts in the ontological framework. This allows
to store and access with the other. The relation between the concepts presents asknowledge to improve the framework from new information. It is described with thewell-defined descriptions such the formal language such XML(s) and RDF(s).

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An ontology is a shared description of concepts and relationships in domain
knowledge. It consists of terms, their definitions, axioms relating to them, and ataxonomy organizing them. The main objective of an ontology is to enablecommunication and knowledge sharing by capturing a shared understanding of terms
that can be used by humans and programs. It has been argued for the use ofknowledge representation techniques capable of reflecting the situated nature ofhuman cognition (Gahegan, W.P.a.M., 2007). It also facilitates the sharing and reuseof information and can reduce the analysis, design, and development time of complex
systems.

Within the body of knowledge to represent, a distinction can be made between
information coming from referential sources and information coming from practicalsources.

Referential sources use documents such as a research paper which provides reliability
to the domain. Practical sources use the working experiences such tasks, activities,
etc. In case study section, the approach will be exemplified by means of two casestudies, one in the educational field (e-learning case study) and one in the governancefield (e-inspection case study).

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Domain	  Level	  Requirement	
  I	
  Requirement	
  II	
  

Requirement	
  III	
  Ontology	
  Level	  Figure 1: This description of LiMe approach.

For both the above cases, information was developed with the cyclical processes.
Firstly, new concepts were defined from individual experiences and formed thestructure of knowledge. This was represented as a tree of concepts. Secondly, eachconcept was linked with the other concepts forming relationships. Users are helped
define and arrange these concepts by the LiMe environment. LiMe introduces
similarity of concepts in the ontological framework and provides the user withdirections for descriptions: generalisation and specification. Therefore, the users candefine the appropriate descriptions for each individual information environment.

Figure 1 illustrates the main spirit of LiMe approach: users characterise their own
requirements on the domain and are helped to represent them in an ontology.
Organisations typically have to deal with lots of information which is unstructured
and difficult to reuse and share.

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Case Studies Outline
In this study, two different organisations, educational and government environment
will be used to show how an ontological approach can help classify information incomplex scenarios.
The e-Learning case study demonstrates the use of referential sources to capture
online learning concepts from research papers to shape the domain knowledge for the
Valaya Alongkon Rajabhat University. E-learning is a good example of an “emerging
domain”, that is a domain which has the particular additional difficulty that thecurrent body of knowledge is not stable (O’Hara, C.B.a.K., 2007). Research into eLearning is currently very active (M del Puerto P., 2008), and the concepts involved
are constantly also being redefined and introduced in different ways. In the casestudy described in this research, human researchers deal with an emerging domain bya process of continuous review of published literature, from which the current
consensus emerges. In the same way, published research papers will be used as the
input resources of this research.

The second case study will demonstrate the use of practical sources to represent the
information that is used for describing the problems in the project inspections task for
the Royal Thai Government. It will later describe these case studies in detail.

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Research problem
In the Internet era, people are using the information from the websites or place thatthey connect to. Technology provides a convenient living style. However, there aresome problems for information developers in case of complex and ever changing,
emerging domains, such as in the government sector or e-learning. The increasing
amount of information, especially internal information such documents, projects,
tasks, requests, etc, contributes to the unstructured nature of information.

The obstacle of accessing the appropriated information needs much more time andhigh development costs. This research will provide processes of information
classification by using an ontology approach. This is the beginning of this research
problem. The research question and problem is described below:l

Research question: Is it possible to organise the information of an emerging
domain by using an ontology approach?

lHypothesis I: The experiences or information from the different people could be
represented with the ontology. These come from the individual person of theorganisation.

l

Hypothesis II: Information from an emerging domain could be used and shared
the information by using the existing ontological framework.

l

Hypothesis III: Semantic Web could be developed from the existing ontologicalframework as input.

In this research, an attempt will be made to define ontologies to facilitate environment
description and represent a complex, frequently changing domain. A tool, LiMe has
been implemented to capture the relevant information from a particular domain. The

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objective is to transfer information and data from paper or oral communication to arepresentation of the knowledge in a computer system.

Literature review
In this research, an ontology development approach is proposed for capturing
information and knowledge in complex domains, such as an emerging domain or adomain involving flexible information, various approaches and methods that change
constantly. E-Learning systems will be used to illustrate a domain of the former kind,
while a government setting will be used to illustrate a domain of the latter. In theimplementation of the research, languages such as XML(s), RDF(s), and OWL areused to describe the domain environment. This chapter reviews the literatures tosupport the research approach. The section has the following four main sections:l

Knowledge: problems such as using knowledge in various platforms, describing
knowledge with different approaches, time to develop knowledge in theorganisation, etc. Knowledge development is introduced to facilitate and solvethese kinds of problem.

l

Ontology: ontology technology could be used to organise the knowledge.

lAn e-Learning system: the e-Learning systems has introduced as domain
example.

l

Semantic Web: it has been used to represent the flexible knowledge informationin the domain.

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Knowledge and Information
Knowledge characteristic has been classified as degree of articulation and aggregation(Cooper, 2007). It is information in the context of other information, such as therelationship between data, information, knowledge and wisdom represented in it.

Knowledge is different from information when it has been used or introduced asproblem solutions. The knowledge definitions are concerning on the goal of the
problem. For example, the knowledge (in term of learning/teaching of online
environment) is the information about the courses in the pedagogical curriculum.
Knowledge is the information which solved the particular problem.

Information is derived from raw data in the events. For example, the registration datasuch as student information, courses registration details, are contributed when thestudents choose the online courses. Information could be constructed from these datasuch as registration table, numbers of the courses that open for selecting,
instructor/teachers/allocating to the courses.

Knowledge has been defined as classification, without the classification human could
be thought, action, or organisation such example of Dewey Decimal Classification(DDC) which is a method that uses in US Library of Congress classification(Wingyan, 2007). Knowledge is unstructured information provided by different
sources such research papers or working experiences. The next section will present
some techniques to manage knowledge.

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Ontology
This research is concerned with building an ontological e-Learning requirements
framework to facilitate the users or the developer to understand and use it forreferencing, describing, searching, retrieving their own environment from theacademic research methods or article as knowledge resources. An ontology specifies a
common conceptualisation, independent of data model, and this may be presented as
Semantic Web. It extracts data user contributions, and captures data as people share
their knowledge in terms of classes and relations between classes. It represents
existing things by illustrating and structuring the knowledge from important
vocabularies. Basically, people adopt their vocabularies to the ontologies. Then,
description languages such XML(s), RDF(s), OWL have been introduced to encode
the structured data and tie it with common vocabularies as classes, properties, andrelations with well-maintenance namespaces.

The domain will be represented as a common framework and helps to integrate or
exchange data from multiple resources. The consistent knowledge of a specificdomain environment is captured and combined with different information sources.
Then, a reasoning approach is needed to support to interpret this framework as
semantic knowledge.

In ontology, the characteristics of an interest domain have been described as concepts
or entities, properties of the concepts, and relations between concepts that include theconstraints (Patil, 2005). Thus, it will be used as value-mapping (support the various
format or data), and scalability (depending on the context of data) (N.Huhns,
K.M.a.M., 1997).

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An ontology is a specification of a concept or property as knowledge (Sheng, 2004) ora concept framework (Zhang, 2006) and content management that consists of fiveprimitives (Wang, 2006): class, relation, function, axiom, and instance. It specifies aconceptualisation of a domain in a term of concepts, attributes, relations, instances,and theories. A concept is a set of individuals or objects in a domain. An attribute isused to depict an intrinsic feature of objects. In addition, the domain scopes orobjectives of the domain will be described with concepts and relations. Semantictranslation determines the similarity between terms as instances of different domains
and maps instances from one to other.

In practical terms, Semantic Web technology uses Ontology abilities to communicate
between human and computer by providing an explicit specification for theconceptualisation of the existing domain. The classic Web will be extended with themeaning of concepts on Semantic Web which could also be shared and reused.

Next, it will explain examples of the research areas that used ontologies to describe
their domain environment.

In Information Retrieval systems (Hwang, M.K., 2007), ontology is used to create,query, inference, and management information that help users to edit, delete, andmodify the existing knowledge in the domain. In order to retrieve the informationfrom the ontology, the reasoning and processing will be used in the query engine.

For example in the tourism domain (Dai, B.A.W., 2005), it is not only informationsuch as the accommodation profiles (details, facilities, etc) that is annotated with RDF

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metadata which could be retrieved but also

tourism information such as water

quality, places, etc could be annotated as semantic data and used for intelligent
search (Sebastian Hübner, R.S., 2004).

Wingyan (2007) proposed Web directories to use ontologies to organise voluminous
information into hierarchical structures, and help users to quickly locate relevant
information and to support decision-making.

E-Learning
An e-Learning system is an education system that is provided in an online
environment, usually via the Internet. Various related terms include virtual classroom,
online learning, web-based learning, computer based learning, web instructions, etc.

The use of the Internet in education has the potential to motivate students andteachers, increase student participation and interaction in the classroom, and provide
students with a more active role in their learning and increased autonomy in the
educational process. While teachers are requested to use the capability of the newhigh technology to facilitate learning processes, students are encouraged to improvetheir learning through computer and networked-based activities.

For example, the Ubiquitous e-Learning (Norm Friesen, R.M., 2005) is a formal
education which not only outside the classroom but also outside the education
environment such as workplace, street, home.

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In addition, an e-learning environment (Norm Friesen, 2005) regards teaching as acontinuous process transferring knowledge with delivery in different forms such as
offline and online learning; self paced and live learning; structural and unstructured
learning; formal and informal learning.

With LiMe, a learning environment to be developed as e-Learning will be designed
and organised, and the environment based on individual requirements. These
requirements will be transformed as a common understanding framework whichavailable to be modified by each user. The different facilities such human or
knowledge experiences, technologies, learning materials, etc could be solved by using
this common understanding framework.

Semantic Web
Semantic Web has been used to produce a semantic context-aware knowledge
management framework that enables to integrate knowledge discovery, retrieval, andreuse (Norm Friesen, 2003).
Semantic Web technologies use smart tools to assist the system administrators tomanage and control various kinds of problem. The requirements of the domain
environments could be represented without misunderstanding by extracting andmodelling the knowledge from the various documents and using Ontology to accessand manage knowledge. Consequently, the common understanding of concepts ispresented as semantic knowledge.

One of the most important aspects of the Semantic Web is searching knowledge fromontologies. Rules of representation have been designed in machine understandable

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form (Nenad, 2002) facilitates to achieve the semantic information. However, it needs
mechanisms and background knowledge about the domain for processing on
ontologies such as updating or adopting their knowledge and reasoning strategies.

Liverpool MetadataThis chapter will illustrate the methodology to build and share ontologies forrepresenting an Emerging Domain such as the e-Learning requirements domain, andwill introduce ‘LiMe’ (Liverpool Metadata), as a means to facilitate the description of
the Knowledge Information.

The idea of LiMe is to provide the descriptors or concepts which represented
knowledge that obtained from research papers. In an emerging domain, the research
papers provide the only effective knowledge resources, and using an ontology enables
to describe this as knowledge information from them.

LiMe presents the knowledge specification of domain environment and provides theability to share and reuse knowledge, providing a common understanding among
different perspectives. People often give different names or definitions for the samething, or different things can be described with the same definitions. An ontology
aims to help this kind of problem.

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Aim
The characteristic of an Emerging Domain (ED) is that the information necessary todescribe it is not fully established, and hence it is difficult to describe and present, andneeds methods that will reflect the changes which will occur as the domain develops
and matures.

Knowledge/Information Ontologies provide the knowledge or descriptions that arenecessary for developing the domain environment descriptions. The aim is to captureknowledge information from the research papers and convert to Web Description
Language such as XML(s), RDF(s), and OWL.

It represents Knowledge/Information as Ontological framework, in which Concept
Ontologies provide the concept characteristics which are represented as a concept
framework that specifies conceptualisations of the knowledge. Representation
Ontologies use the Semantic Web to illustrate the domain environment based on an
ontological framework.

Characteristics
Methodologies used to develop an ontology have five different techniques: frames
and first order logic, description logic, software engineering, and databases (GomezPerez, 2004).

LiMe uses the database technique and presents the domain with hierarchy of concepts
as tree in the figure 2. It has been designed to store knowledge from information orparagraphs of the research articles. Both information and paragraphs are called

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Knowledge/Information (KI) which is a consensual knowledge used to extract theconcepts and their properties as Object Oriented modelling.

Environment will be organised and represent the characteristic of the domain. The
particular environment is the subsystem or sub-organisation that represents thefunctions in the domain. For example, in e-Learning domain, it consists of learning,teaching, and management.

Knowledge/Information in the particular environment is used as referencing resourcesthat defined concept, properties, and instances. This information also facilitates todefine the relation between concepts. Relation is the relationship between two or moreconcepts. LiMe classifies the relation in two different relation categories:
specification and generalisation. It also presents the semantic meaning direction.
Specification is the top-down approach and generalisation is the bottom-up approach.
Both approaches are used to develop trees or taxonomies that are called ontologies inthe domain. A circumstance of domain uses ontologies to exchange the common
understanding and give as a structure framework.
LiMe methodology organises domains as a five-level taxonomy. For example:l

Domain: e-Learningl

Environment: Learning, Teaching, Administration, Infrastructure, etc.

lKnowledge/Information: definitions or meaning, functions, Examples, etc.

lMetadata: Learning Material, Student, Teacher, Learner, Instructor, etc.

l

Properties: Learning process, tasks, etc.

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LiMe Resources
LiMe illustrates a hierarchy of research papers as Web resources and Knowledge fordeveloping the Ontology of a particular domain environment. A research paper isorganised with two parts: Reference Resource and Knowledge. It is introduced as
Web Resources which contain reference information and knowledge. Knowledge will
be classified as Information that is captured from the research paper or the individual
experience which is contributed by the developers.

A research paper contributes information such as research problems, researchmethods, objectives, research results, and conclusion, represented using text, tables,or diagrams. This information is used as Knowledge /Information (KI) for developing
an ontology. LiMe captures KI from the research papers using the individual
experience and background knowledge of the (human) reader. Moreover, LiMe uses
KI to extract or define the concepts that are related to the domain. A concept may be
a general concept or class, a specific concept or property/instance, or a relation
concept that represent the relationship between concepts, instances, or properties.

Instead of searching the knowledge based on keywords from the journal, LiMe
organises knowledge that facilitates to reduce the retrieval time. The unnecessary
article will not be listed. However, the appropriate concepts that facilitate to identify
or describe the knowledge are important, costly, and time consuming process.

LiMe’s Development Cycle
To capture the Emerging Domain (ED), flexible or new concepts are extracted fromthe research domain. LiMe presents these concepts knowledge as Metadata and uses

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to develop ontology. LiMe proposes the development cycle (Figure 2) with four basic
methods: KI identification, Concepts extraction, Ontology development, andRequirements representation.
Knowledge	  Information	
  
KI	
  
Identification	  Metadata	  Extraction	  Semantic	
  Knowledge	  Patterns	  	
  	
  Metadata	  Ontology	  Development	
  Requirements	
  representation	  Ontological	
  Framework	
  Figure 2: This figure illustrates LiMe development cycle.
The information relating to the ED is gathered from research papers. Our aim focuseson transforming the Domain specification to Semantic Knowledge.
Domain specification > Semantic Knowledge

In LiMe, domain specifications will be represented as the requirements from various
researchers that contributed KI included both approaches and results in this researchdomain area. This knowledge will be organised with concepts that extracted from thisknowledge information. Concepts also represent the patterns of knowledge which isused to classify the knowledge categories such as meanings, definitions,specifications, functions, tasks, etc.

An ontology in our research is the knowledge classification. It describes the domain
specification. It translates the KI in each particular environment to ontologicalframework. This framework is the place for interchanging the knowledge in theenvironment and will be interpreted as semantic knowledge with Semantic Web.

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Knowledge identification
In order to extract the knowledge from the research paper, LiMe imports theKnowledge/Information by using the academic journal search engine which the
keywords to gather the domain specification from search engines of the academic
journal websites such as Springer, ScienceDirect, IEEE Xplorer, etc. However, thisphase does not an automated mechanism, human still have to choose and find therelated papers.

This process could take a lot of time especially for non-expert

knowledge domain developer within huge related domain articles are listing.

LiMe describes KI as the crucial information or context information that help theusers (developers, researchers, etc) to understand about the domain where could
locate on paragraphs of paper articles such abstracts terms, definitions, notation,abbreviations, examples, approach, results, experiences, discussion, related topics,
and so on.

In addition, LiMe also introduces the patterns of knowledge such as
meaningful/definitions (descriptions), components (properties, instances), restrictions(relations, condition, constraints), etc. which could be added and improved. LiMe willstore these patterns as KI categories and use them to reduce the time of capturing in
the future. In order to understand, the tasks of Knowledge identification have been
represented as follows:1.

Define the scope or particular environment of domain of interest, which is theobjective for developing an ontology. For example, this research concerns on
describing the e-Learning requirements domain, therefore, the objective is to59

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develop an ontology to annotate requirement in e-Learning domain, to help eLearning researchers or organisation developers.
2.

Define the keywords that related to the domain or scope, such as topics, title,
instances, etc. Instead of only generate keywords from background experiences
consideration, keywords could be found in the LiMe’s thesaurus within theexisting environment framework.

3.

Use the keywords to find the related articles from the academic journal websites.
With the large number of online papers, existing keyword-based searches
retrieve many irrelevant papers that may use a certain word in different contexts;
they might also miss papers when different words about the desired content are
used.

4.

Find the crucial information related to the domain, based on the previous patterns
or categories. A pattern is a kind of context that identifies the relation to thescope, environment, or domain which is not easy to identify. Especially,
different researchers express their knowledge in different ways. Backgroundexperiences of the domain will help to identify the knowledge context from thegeneral information.

5.

Capture the KI from articles and store it to the LiMe system. This KI will alsotranslate to the formal language XML(s). LiMe also captures the article profiles
such as title, author(s), journal, volume, issue, page, and URL. This information
is a reference resource to refer during developing an ontology. Note that LiMe
does not upload the file resource.

6.

Update and improve the pattern identification. All the tasks are repetitive tasks.
LiMe enables the developer to define the patterns which are the contextual
criteria of KI.

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Metadata extraction
LiMe produces sets of Metadata of the domain environment which are extracted from
KI obtained from the research domain articles. LiMe presents Metadata in the term of“concept”. In order to extract the concept from the KI, the follow steps are followed:
1.

Find the general or specific topic such as subject or object in the statement. LiMeis concerned with capturing the definitions, components, or functions from theKI. There are various ways to find the concept in the paragraphs: find thespecific concept, find the general or abstract concept, and use experiences todefine the concept.

2.

Given the type of concept, LiMe has four different concept types: class,properties, relations, and instances. Class is the entity or the existing things ofthe environment in the domain. Properties are the specification details of theconcept. Relations are the relationship between concepts, which are propertiesof a concept. Instances are the example objects for concepts. Some concepts
could be both class and properties. LiMe presents concepts as the Object
Oriented model in Class, attributes and objects.

3.

Define an explicit relation hierarchy between the concepts in the same KI. In
addition, properties, and instances are used to specify characteristic of concept.
LiMe uses taxonomies to organise concepts, properties, relations, and instancesin the ontology. LiMe has relation based on type of the concepts.

4.

Compare this topic with LiMe’s thesauri that provide semantic between concepts
such as synonym relationships. Then, update the new concept to the thesauri. A
concept might take different assumptions from different perspectives and be usedin different areas. In order to clarify the definition, LiMe proposes the existing61

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concepts with an ontological framework that could be specified the definition for
creating the new concept in the thesauri.
5.

Generate tree or taxonomy of the concepts to represent knowledge and alsotranslate this taxonomy to formal description language such as XML(s), andRDF(s). Therefore knowledge is represented with one or more taxonomies froma particular KI as independent descriptions.

At this step, LiMe produces Metadata that will be used to describe the knowledge
from KI. LiMe has classified Metadata based on the three basis functional types
described from the statements in KI. Descriptive Metadata is a concept that describes
the information such as meaning, definitions, etc of the knowledge. Structural
Metadata is a concept that classifies or structures information of the knowledge.
Finally, Administrative Metadata is a concept that describes information such as
constraints, conditions, rules, etc of the knowledge. A set of concepts extracted fromKI will be represented in this task.

Ontology development
LiMe proposes to develop an ontology for representing the Emerging domain. The
development process is mainly integrating the taxonomies constructed from KI of
research articles. An Ontological Framework (OF) is the result of this method. It
enables developers to communicate and interact to the Emerging Domain bycontributing the common understanding of concepts. This is a structure informationthat objective, accessible reusability, and flexible accomplishment.

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In order to develop an ontology, LiMe proposes two basic processes: Similarity
measurement, and Taxonomies integration as following.

Similarity measurement
LiMe uses the similarity between two concepts to reduce the redundancy and presents
consistent concepts. Similarity could be easily detected by humans, whereas
computers need to evaluate parameters to identify the similarity.

Currently, there are some methods that contribute to similarity algorithms such as
Information-based similarity (Al-Mubaid, 2006), functional and textual based method(Ganjisaffar, 2006), and similarity graph (Andreasen, 2003). And, the relationships
between concepts could be described in the terms of Synonymies, Hyponymies, andOverlapping (Maria Ruiz-Casado, 2005). Synonymies denote that two or moreconcepts have the same meaning. Hyponymies denote that a concept has more than
one meaning. Overlapping indicate that concepts are neither synonymies nor one
hyponymy of each other, but represent to some extent the same reality.
LiMe proposes to use the combinations of two techniques to compute the similarity
between concepts: first, using the weight of the concepts, and secondly, using the
distance between concepts.

Weight-based technique. This similarity method described in (Ganjisaffar, 2006) isbased on functional and textual information. The concept similarity function
calculates, from a pair of concepts, a real number between 0 and 1, expressing thedegree of similarity between two concepts, based on two characteristics: Taxonomy

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based concepts and weight Information Content. The “1” value indicates that a pair ofconcepts are strongly similar whereas “0” indicates that they are different.

Edge-based technique. This similarity technique counts the edges between concept
c1, and c2. For example, (Zhumin, 2006) describes Wu and Palmer algorithm that
calculated the similarity between concepts as following.
Sim(c1,c2) = 2 x / ( x + 2 x )Where and are the length of the path from c1 and c2 to their most specific common
super-concept c3, and is the length of the path from c3 to the root of the hierarchy.

Taxonomy integration. In order to integrate taxonomies from a domain, LiMe focus
on a similarity measurement. The relation between common concepts will be definedwith “is-A” and “part-Of” relationships. The “is-A” relation is used to express that apair of concepts have fully similar characteristics. The “part-Of” relation is used toexpress that there are partly similar characteristics between two concepts.

LiMe uses both a Top-down and a Bottom-up approach to integrate and express theknowledge direction in taxonomies. Top-down approach is used to annotate a more
abstract concept with the specific existing concepts. This could be extracted not only
from the research papers but also provided by domain experts as their backgroundexperiences.

To optimise taxonomies, LiMe uses Term matching technique patterns (Asanee,2004) that integrate the similarities concepts to structure, form, or extend thetaxonomies with four different cases.

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A term matching technique is used to integrate a taxonomy which has a concept that
could be expressed with relations to a different taxonomy. In additional, a consistency
concept is the similarity between concepts in different taxonomies and could be
expressed with the”is-a” or “part-of” relation. LiMe calls an existing taxonomy that is
extended with a consistency concept as a core hierarchy.

Requirements representation. LiMe is concerned with representing Knowledge thatis constructed from individual user perspectives. LiMe defines knowledge provided
from the individual research article as requirements.

In order to provide a dynamic or flexible representation, a distinction can be made of
the source contributing knowledge in two basic types: the reference requirements andthe user-defined requirements. This will allow the users or developers to have aflexible opportunity to define their knowledge as knowledge template.
Research	
  Knowledge	
  

User	
  
KnowleReference	  requirements	  User-­‐Defined	
  requirements	  Knowledge	
  Blog	  Interactions	  Ontological	
  Framework	
  Composed	  by	
  Description	
  Languages	
  Taxonomies	
  Figure 3: Knowledge Blog in LiMe.

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The reference requirements are perspectives on the knowledge, the information fromthe research paper or articles. The user-defined requirements are contributed by theusers or developer in the organisation and will be used to understand the backgroundknowledge about the domain. It is possible that they do not have the knowledge orunderstanding about the domain environment.

With the various different knowledge perceptions, a flexible representation approachis required to handle the various information formats. This work proposes a semantic
blog, the Knowledge Blog (KBlog), to organise and describe the different
understandings of the Ontological Framework in the domain (Figure 3).

The main contribution of the Knowledge Blog is the idea of using a Blog to present
the conceptual knowledge. With Blog technology, the knowledge contents aregathered from the individual requirements and research contributions on the web. The
KBlog provides the interface to the knowledge of the domain as mechanism ofknowledge annotation and facilitates the users to look and find across the blog
comparing their knowledge with the others.

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Case Studies
In order to illustrate how to use the ontological Framework, this section will present
the results from two case studies: an educational and a government based case. LiMehelped produce the ontological framework which was used to develop Semantic Web
solutions in each domain.

The following evaluation has been carried out, and feedback was obtained on thedevelopment. These web based applications are fully implemented and have beenused in a real environment.

Teacher environment
LiMe has been introduced to support the web development for Valaya Alongkorn
Rajabhat University (VRU). This university had attempted to implement an eLearning policy but this did not work in their environment. LiMe was employed to to
help improve the teaching and learning environment.

In order to do this, LiMe started from the teaching environment. It mainly supported
the teachers in the grading system. In this environment, the activities of students andteachers are homework submission, class attendance, online exam, and grading.

The web developer used LiMe to find useful concepts of the teaching environment
from the ontological framework and improved the framework with their teacher
working experiences. This provided suitable design requirements and a clear picture
before developing the software. Teachers had the opportunity to contribute theirrequirements.

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LiMe was especially useful to reduce the time of the requirement collection. The, it
was used to present the environment structure. This structure could be modified or
improved to accommodate the individual requirements. The following figure (Figure4) represents the screen snapshot of the application that used LiMe to design andorganize information from the ontological framework.

Figure 4: web-based application in the teacher ontological framework.

Faculty environment 1
In this experiment, LiMe has been used to develop the information management for
the faculty environment in Thailand. It provided the ontological framework that
represented information about faculty. Figure 5 shows the Faculty of Science andTechnology at VRU. Faculty improved or modified this framework from theirrequirements. LiMe improved the budget management framework for every section in
the faculty.

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LiMe also provided useful features in the operation patterns. It helped define services
for each member in the section as service framework. With LiMe, level of services
not only is classified but also related to the relevant information from the type ofmember of staff using the system.

Figure 5: web-based application in the Faculty environment

In addition, the users described their projects within the ontological framework
developed from the university framework. This helped establish the required
interoperability between the faculties in the university. The process of implementing
university strategies and monitoring project quality assurance was also improved.

Faculty environment 2
In this experiment, an ontological framework has been applied to develop andorganise the information for the inspection system of the Royal Thai Government(Figure 6).

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In Thailand, projects are created from the organisation of Ministries. Many projects
contribute to a budget plan. Projects need to be tracked to make sure they are
implemented correctly also in remote project areas such villages, and provinces. The
inspection serves not only to monitor the processes but also to provide relevant
information to the project owners.
LiMe has been used to collect the requirements and design the model of thegovernment inspection. This model will be deployed in the real environment.
Therefore, these requirements are very important and need a suitable structure of
information to support in the inspection process.

Figure 6: The inspection in Thailand.
Practically, the government inspection has been designed in five processes: plan
direction, plan preparation, investigation, report, and knowledge management. An
ontological framework was developed for organising and retrieving the informationneed in the system. It also helped to classify the project problems collected from70

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The common understanding of the projects was provided to theinspectors. This information was stored as the central information sources.

The Plan Direction process is the defining process. Problems coming from previous
projects will be addressed and used to find solutions or improvements. It involves
document classification, risk analysis, and inspection background information. This is
the useful information to support the inspectors from the remote area.

The Plan Preparation process is the plan creation. The inspectors will design the tasks,
problems, and schedules required by the projects. The relevant information about the
remote area, such as contact information, activities, requesting, and others, will beorganized to support the task. The topics that needed further information will be
developed. The most important information is the project details.

The Investigation process is the data collection process. Information has been
captured from the remote areas by the government inspectors. Suggestions andsolutions will be provided. These are the results of the project operations. Feedback
from the projects is stored in the structured information.

The Report process is the results representation. In order to improve the projects, allinformation that captured from the remote areas is provided. Different perspectives of
the information will be developed and also the comments or suggestions will be addedin this process. LiMe applied the ontological framework in the report system. It
provided the report designing for the users which allowed modifying the reporttemplates based on the individual requirements.

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Lastly, the Knowledge Management process is the core process that applies to every
process. It involves information classification. Knowledge is the information used tosolve the problem. This is fully supported from LiMe methodology which captured
the information, extracted the concepts, developed the ontological framework, andtranslated it into formal languages.

Basically, the different inspectors introduced different meanings for the information.
This is the feature the ontology approach was most useful with. The common
understanding of the information will be useful to the environment. The accuracy ofaccessing the right information from the existing framework was very useful.
Knowledge itself could be improved from the descriptions. Therefore, this model willbe the more successful, the more members are participating to it. For this reason, thesystem was designed the experiment as a social network (Figure 7), where members
can interact by sharing knowledge, experiences, problems, suggestions, comments,
etc, not only as text but also images, and video clips.

Figure 7: This inspector application designing

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Conclusions
Results
This research contributed various terms or methods to this research. For example, theterm “Knowledge Information” was introduced to represent the crucial informationthat is extracted from research articles. This KI has been captured and described basedon the individual perspectives from the researchers. It is very useful information,
especially for the researchers that require the articles related to their research areas.
Instead of searching the academic journal websites, they can use this information to
retrieve the related information and access the articles from the journal websites.

LiMe has classified knowledge into two different kinds: User-defined requirements
and Knowledge Information. The User-defined requirements are knowledge that iscontributed by the ontology developer, or domain expert. Knowledge Information isknowledge that is captured from research articles. LiMe represents knowledge bydeveloping the combination between knowledge in an Ontological Framework.

An Ontological Framework is the intermediate information that provides the
specification of knowledge in the domain. It has been represented with a hierarchy ofconcepts which is called taxonomy. LiMe integrates the taxonomies in the domain ofinterest based on the knowledge topics.

In order to interact with this framework, LiMe proposes the Knowledge Blog to aid
the knowledge representation. It has been developed for retrieving, describing, andanalysing the knowledge from the domain.

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Future Works
LiMe has been proposed as an open environment methodology which extends current
methodologies. It is still in the development stage. Therefore, it is available to any
developer wishing to use this methodology to develop any Ontologies in any researchdomain.

To perform at its best, LiMe needs a lot of information about the domain. Moreknowledge information will produce more Metadata to describe the domain.
Therefore, LiMe needs a way to integrate Ontological Frameworks, and it could be
improved by applying results from the ontology community working on Ontology
merging, Ontology mapping, and Ontology alignment methodologies.

LiMe classifies the knowledge based on the individual topics. Therefore, flexible
information will be represented in different ways. Similarity methods are needed toresolve the problems of inconsistency in the Ontological Framework.

In practice, LiMe uses description languages such XML(s), RDF(s), and OWL toshare and reuse an Ontological Framework. The specification of these languages orversioning will enhance the reliability to describe the context and characteristics ofknowledge in Ontological Framework for individual environment domains.

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