IEEE Paper Template in A4 (V1)
Developing a Community-Based Knowledge
System: A Case Study using Sri Lankan
Agriculture
Anusha Indika Walisadeera*#1, Athula Ginige$2, Gihan Nilendra Wikramanayake#3
*University of Ruhuna, Matara, Sri Lanka
1waindika@cc.ruh.ac.lk
$School of Computing, Engineering & Mathematics
University of Western Sydney, Parramatta Campus, NSW, Australia
2a.ginige@uws.edu.au
#University of Colombo School of Computing, Colombo 07, Sri Lanka
3gnw@ucsc.cmb.ac.lk
Abstract— The Agriculture sector plays a vital role in Sri
Lanka’s economy. Not having an agricultural knowledge
repository that can be easily accessed by people in agriculture
community in Sri Lanka within their own context, is a major
problem. As a solution, a large user centred ontology for Sri
Lankan farmers was developed to provide required
information/knowledge not only in a structured and complete
way, but also in a context-specific manner. Since this problem is
not only limited to farmers, we extend this for every one
working in the agriculture domain. We validate the ontology in
terms of accuracy and quality. The online knowledge base based
on the ontology with a SPARQL endpoint was created to share
and reuse the domain knowledge that can be queried based on
user context. A Mobile based application and a Web based
application were developed to provide information/knowledge
by using this ontology. These applications are also used to
evaluate the ontology by getting the feedbacks from users to the
knowledge in the ontology. It is very difficult to maintain a large
complex ontology. To maintain our ontology, we identified
various processes that are required to develop and maintain
ontology as a collaborative process. A semi-automatic end-to-
end ontology management system was developed to manage the
developed ontology and the knowledge base. It provides the
facilities to reuse, share, modify, extendand prune the ontology
components as required. The facilities to capture users’
information needs and search domain information in user
context are also included. In this paper, we present a summary
of the overall development process of the ontology including the
end-to-end ontology management system.
Keywords— Agricultural Information/Knowledge, Contextual
Information, Knowledge Modeling, Ontology, Ontology
Management Systems.
I. INTRODUCTION
Agriculture is an important sector in the Sri Lankan
economy. 31.8% out of the total population in Sri Lanka
engages in agricultural activities [1]. People in agriculture
domain, need agricultural information and relevant
knowledge to make informed decisions and satisfy their
information needs. For example, farmers need information on
pest and diseases, control methods, seasonal weather, best
varieties or cultivars, seeds, fertilizers and pesticides, etc. to
manage their farming activities [2], [3]. Other stakeholders of
the domain such as agricultural instructors, researchers,
information specialist, policy makers, etc. need agricultural
information to fulfill their information needs. For example,
researchers are interested to know the information about how
to solve the problems of pest, symptoms of crop diseases, and
usage of fertilizer and pesticides for research purposes.
Agricultural instructors also need domain-specific
information to help farmers in their region. Thus, all the
stakeholders in the agriculture community need agricultural
information relevant to them to make better decisions, do
further research, or analyze the information for future needs
and predictions. They can get some of this information from
multiple sources such as agricultural websites, agriculture
department leaflets and mass media, etc. However the
information in the above sources is general, incomplete,
heterogeneous, and not structured to meet their needs. They
require information within the context of their specific needs
in a structured and complete manner. Such information could
make a greater impact on their decision-making process [4].
Not having an agricultural knowledge repository that is
consistent, well-defined, and provide a representation of the
agricultural information and knowledge needed by the
farmers within their own context, is a major problem.
Moreover, this problem is not only limited to the farmers, it
effects every one working in the agriculture domain.
Social Life Networks for the Middle of the Pyramid
(www.sln4mop.org) is an International Collaborative
research project aiming to develop a mobile based
information system to support livelihood activities of people
in developing countries [5]. The research work presented in
this paper is part of the Social Life Network project, aiming
to provide agricultural information and knowledge to farmers
based on their own context in Sri Lanka using a mobile based
information system. This system has now been expanded to
include everyone working in the agriculture domain in Sri
Lanka through a development of an end-to-end ontology
management system via web based interface.
To represent the information in context-specific manner,
firstly, we need to identify the users’ context (i.e. users’
context model). Since the farmers are the main stakeholders
in the agriculture community and other stakeholders are
willing to help farmers in various manners, we have
identified the users’ context specific to the farmers in Sri
Lanka such as farm environment, types of farmers, farmers’
preferences, and farming stages [6]. The farming stages that
we have identified as relating to our application are Crop
2
Selection, Pre-Sowing, Growing, Harvesting, Post-
Harvesting, and Selling [6].
Next we have identified an optimum way to organize the
information and knowledge in user context using ontologies.
An Ontology provides a structured view of domain
knowledge and act as a repository of concepts in the domain
[7]. The most quoted definition of ontology was proposed by
Thomas Gruber as “an ontology is an explicit specification of
a conceptualization” [8]. Mainly due to the complex nature
of the relationships among various concepts, attenuate the
incompleteness of the data, and also add semantics and
background knowledge about the domain we have selected a
logic based ontological approach to create our knowledge
repository.
We first developed an ontological approach to represent
the necessary agricultural information and relevant
knowledge within the user context [6]. Using this
approach,we designed the ontology to include information
needs identified for the first stage of farming life cycle [9].
Next we extended the ontology to include events associated
with the farming life cycle such as fertilizers, growing
problems, and their control methods [10]. A revised and
enhanced version of the work including the creation of an
online knowledge base and an information retrieval interface
has been published in [11]. In this paper we have presented a
summary of the overall development process of the user
centered ontology and the end-to-end ontology management
system with respect to the domain of agriculture in Sri Lanka.
The user centered ontology was implemented using protégé
editor (based on OWL 2-DL). A Web-based ontology
management system was developed based on the framework
explained in [12].
The remainder of the paper is organized as follows.
Section 2 summarizes the development process of the
ontology. A summary of end-to-end ontology management
system is explained in section 3. Finally, section 4 concludes
the paper and describes the future directions.
II. ONTOLOGY DEVELOPMENT PROCESS
To clearly identify the process of the ontology
development to represent the information in user context,
this section (section II) is mainly organized in six (6)
categories such as users’ information needs, users’
information needs in context, representation of contextualized
information, generalizing design approach, and validation
and evaluation process. The framework we identified to
maintain the ontology for our application is described
separately in section III.
A. Users’ Information Needs
First we have extracted domain specific knowledge using
the reliable knowledge sources [2], [3], [13]-[17], by
interviewing the farmers as well as other stakeholders in the
agriculture community. By analyzing the information
gathered from various sources, we have identified what
information is required by the users in agriculture domain at
various stages to support better decisions, problem solving,
and other information needs. As a result of this analysis,
information important to users was identified in the form of
questions. Some examples are given in Table I.
TABLE I
USERS’ INFORMATION NEEDS
Users’ Information Needs
What are the suitable crops to grow?
What are the best varieties (or cultivars)?
What are the best fertilizers for selected crops and in what
quantities?
When is the appropriate time to apply fertilizer?
What are the types of pests or crop diseases?
How to solve the problems of pests?
What are the symptoms of crop diseases?
How to solve crop diseases?
Which are the most suitable control methods to a particular
disease?
What are the problems of pesticides?
What are the reasons for reduction of yield and/or quality of
the specified crop?
How to control diseases in an environmentally safe way?
What are the best techniques for harvesting?
What are the crops cultivated by other farmers and in what
quantities?
In this study we identified that, farm environment, types of
farmers, farmers’ preferences, and farming stages
(considered as the user context model) are the important
factors that need to be considered when delivering
agricultural information and knowledge to farmers [6].
B. Users’ Information Needs in Context
We identified areas of generic crop knowledge required to
answer the users’ information needs (see Table I). We have
called these broad areas of knowledge as “knowledge
modules”. The generic crop knowledge consists of modules
such as nursery management, harvesting, post-harvesting,
growing problems, control methods, fertilizer, environmental
factors, crops and basic characteristics of crops, variety, etc.
For example, crop module has information about crops and
fertilizer module has fertilizer information and knowledge to
handle the fertilizer knowledge needed by domain users. Next
we identified the relationships among them. The Fig. 1 shows
the generic crop knowledge module. This modularization also
helps us to reduce the complexity of real-world scenario in
the application domain. It is very hard to maintain a large
ontology. Furthermore, this modularization assists us to
maintain a large ontology by maintaining small blocks in the
knowledge module.
Fig. 1 Generic Crop Knowledge Module
We organized the users’ list of information requirements
according to the farming life cycle stages (6 stages - Crop
Selection, Pre-Sowing, Growing, Harvesting, Post-
Variety
Nursery
Management
Environmental
Factors
Harvesting
Growing Problems
Growing Practices
Control Methods
Post Harvesting
Symptoms
Basic
Characteristics
Fertilizer
Crop
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Harvesting, and Selling). We begin our detail design process
with the first question in the list; “What are the suitable crops
to grow?” Choosing the best crop for individual situations is
difficult since one has to consider many factors such as
environmental conditions which can vary based on region
and time period, preferences of user, and resources available
for them for cultivation. We therefore have reviewed existing
literature on crop selection to identify a suitable criterion
which can be used to make better decisions. Then we
summarized the existing criteria and identified a suitable crop
selection criterion for our application based on the
requirements of agriculture community in Sri Lanka [11]. It
includes the environmental conditions, the special
characteristics of a crop, user preferences, about what other
farmers grow in different regions and its quantities, and the
market information.
In a similar way, we identified the criteria for each item in
the list of user information requirements. For example, we
defined the criteria for applying fertilizers to deliver fertilizer
knowledge and for the growing problems and their control
methods related to second stage and third stage of the
farming life cycle respectively. When applying a fertilizer for
a specific crop user needs to know fertilizer quantity and its
unit. A fertilizer quantity depends on many factors; especially
it depends on the location, water source, soil Ph range, time
of application, application method, and fertilizer type. In
addition to this information; the cost, the land sized required
for particular fertilizer, and other special information need to
be considered. Thus fertilizer quantity needs to be specified
in relation to all these information. To do that, we introduced
a new information module; Fertilizer Event to represent this
additional information and new relationships to describe this
event. More details about the criteria for applying fertilizers
and selecting control methods are explained in [10]. A
summary of these criterion factors is shown in Table II.
TABLE II
SUMMARY OF THE CRITERION FACTORS FOR CROP SELECTION, FERTILIZER APPLICATION AND CONTROL METHODS
Crop Selection Fertilizer Application Control Method Selection
Environment
Soil
Location
Water Supply
Season
Crop Characteristics
Hardiness, value added products,
etc.
Length, weight, color, shape,
quality, size of the variety
Etc.
User Preferences
High yielding varieties
Maturity time and disease
resistance
Other preferences
Labor Requirement
Market information
Other farmers’ information
Environment
Soil
Location
Water Supply
Time of Application
Pre-Sowing stage
Growing stage, etc.
Application Methods
Basal dressing
Top dressing 1
Top dressing 2, etc.
User Preferences
Fertilizer types such
as chemical,
organic, or
biological and its
specific sources
Farm land size
Budget
Environment
Soil
Location
Water Supply
Farming Stage
Application Stage
o Before
Infestation
(Avoid and
Prevention)
o After Infestation
(Control)
User Preferences
Control Method Types
such as chemical, cultural
and biological control
methods
The next step is formulation of a set of contextualized or
personalized information based on the users’ information
needs. For this, we had to develop our own approach to
formulate the contextualized information. With the help of
the domain experts, we first identified the breadth of
information required by users. Next based on earlier
identified user context we identified the conditions we can
use to obtain a subset of information that can satisfy a
specific information need of users. Based on this, we
expanded the questions in the user information need list to
include the user context.
The Fig. 2 shows our basis for formulating contextualized
information. The formulation of contextualized information
for crop selection depends on multiple criteria such as the
users’ context, general crop knowledge, crop selection
criteria (select a suitable task modeling criterion specific to
the question; for example crop selection criterion, fertilizer
application criterion, control method selection criterion, and
so on) and the users’ constraints (conditions). This serves as a
basis for formulating information in a user context for our
application.
Fig. 2 Basis for Modeling Contextualized Information
Some examples of contextualized information related to
each category of crop selection, fertilizer applying, and
control method selection are given in Table III. We have
Generic Crop
Knowledge
Module
Additional
Knowledge
Modules
Task Modelling
Criteria
User Context
Model
Contextualized or Personalized Information
4
identified the user constrains based on the each criterion
factor. We therefore need to select suitable information based
on the different locations, different seasons, different soil
factors, different types of control methods, etc. or
combination of these constraints that help to make better
decisions. We have identified these different constraints
related to this application. For example, we identified the
location as a Zone, Agro Zone, Elevation based location,
Province, District, and Regional area (see Fig. 3 (a)). The
relationships among these are also complex based on the
meaning of these terms. For example, Agro Zone is a Zone,
Zone is a Location, Variety is a Crop, and the representation
of the environmental factor (see Fig. 3 (a)). The definitions of
the terms also need to be considered to attenuate the
incompleteness of the data (see Fig. 3 (b)). Furthermore, we
need to represent semantic meaning of the terms, for example,
if Magalle (location) belongs to Galle (location) and Galle
belongs to WetZone (location) then Magalle belongs to
WetZone (see Fig. 3 (c)). Through this process, we have
formulated the contextualized questions covering all
constrains relevant to each criteria. We also generalized these
questions (see Table III).
TABLE III
USERS’ INFORMATION NEEDS IN CONTEXT
Users’
Information
Needs
Users’ Information
Needs in Context
Generalizing
Contextualized
Information
Stage 1: What
are the suitable
crops to grow?
Suitable crops based on
the Environment:
What are the suitable
vegetable crops for
‘UpCountry’, applicable
to the ‘Well-drained
Loamy’ soil, and
average rainfall > 2000
mm?
Suitable crops based on
Preferences of Users:
What Brinjal’s varieties
are good for the
‘Bacterial Wilt’ disease?
Suitable crops based on
Environment,
Preferences and Other
Information:
What is the best
Brinjal’s variety which
is suitable for ‘DryZone’
and high-resistance to
the ‘Bacterial Wilt’
disease?
What are the suitable types
of crops for specified
location (Elevation),
applicable to the specified
soil types/characteristics,
and conditions (Rainfall or
Temperature)?
What crop’s varieties are
good for the specified
disease?
What is the best crop’s
variety which is suitable for
specified location (Climatic
Zone) and resistance
conditions to the specified
disease?
Stage 2: What
are the suitable
fertilizers for
selected crops
and in what
quantities?
Suitable fertilizers based
on the Environment:
What are the suitable
fertilizers and in what
quantities for farmers in
Badulla district who
cultivate Tomatoes?
Suitable fertilizers based
on Preferences of Users:
What are the suitable
organic fertilizers which
are used to Basal
dressing for Tomato?
What are the suitable
fertilizers and in what
quantities for farmers in
specified location (Districts)
who cultivate specified
crops?
What are the suitable types
of fertilizers which are based
on method of application for
specified crops?
Stage 3: Which
are the most
suitable control
methods to a
particular
Suitable control methods
based on the
Environment:
What are the suitable
control methods to
What are the suitable control
methods for different types
disease? control weed for Radish
which is grown in Up
Country?
Suitable control methods
based on Preferences of
Users:
What are the suitable
chemical control
methods and in what
quantities to control
Damping-off for
Tomato?
Suitable control methods
based on the Farming
Stages:
What are the suitable
control methods to
control Bacterial wilt for
Brinjal before
infestation of the
disease?
of growing problems to
specified crop which are
grown in specified location?
What are the different types
of control methods to
specified growing problem
of a crop?
What is the suitable control
method based on the
specified farming stages to
specified growing problem
of a crop?
These are the range of questions that we want to obtain
answers by organizing agricultural information and
knowledge to query in context using ontology.
C. Representation of Contextualized Information
An ontology provides a structured view of the domain
knowledge and act as a repository of concepts in the domain.
This structured view is essential to facilitate knowledge
sharing, knowledge aggregation, information retrieval, and
question answering [7]. Mainly due to the complex nature of
the relationships among various concepts, attenuate the
incompleteness of the data, and also add semantics and
background knowledge about the domain (see Fig. 3) we
have selected a logic based ontological approach to represent
the contextualized information/knowledge (in Table III) that
can be used to find a response to queries within a specified
context in agriculture domain.
We reviewed ontology development methodologies and
techniques to identify a suitable ontology development
approach. Grüninger and Fox [19] have published a formal
approach to design ontology while providing a framework for
evaluating the adequacy of the developed ontology. We
therefore selected Grüninger and Fox’s methodology, a logic
based approach to develop a user centric ontology for
agriculture community.
Our ontology creation begins with the definition of a set of
users’ information needs identified in Table I. We take these
information needs as the main motivation scenario of our
application to provide information in context. Competency
questions (CQs) determine the scope of the ontology and use
to identify the contents of the ontology. The ontology should
be able to represent the CQs using its terminologies, axioms
and definitions. Then, a knowledge base based on the
ontology can provide answers to these questions [19].
Therefore, formulation of the CQs is a very important step
because these questions guide the development of the
ontology. In our application, the contextualized information
(see Table III) has been used as the CQs to develop the
ontology because it satisfies the expressiveness and reasoning
requirements of the ontology (see Fig. 3).
The different constraints in the domain are represented
using OWL-2 DL (see Fig. 3). Fig. 3 (a) represents the
semantic meaning of the concepts using the class hierarchies.
5
The sub concepts inherits the properties of the parent
concepts and then instances of the sub concept act as the
instances of the super concept, because of the taxonomic
hierarchy (is-a relationship). The definition of the concept,
for example DryZone is represented in Fig. 3 (b). The
instances need to be classified based on these definitions. The
reasoner attached to the protégé tool can be used for this
classification. By using the transitive property, the relation
belongsTo with respect to the instances of the Location
concept is defined and shown in Fig. 3 (c). Based on the
existing information, the additional knowledge can be
inferred using the composition of relations (e.g. the relation
GRANDFATHEROF is composed by the relations
FATHEROF and PARENTOF). We used this property to
infer the additional knowledge (see Fig. 3 (d)). The object
property chain in Protégé tool is used for this representation.
(a) Class Hierarchies
The concept definition of the DryZone:
x (Zone(x) (y, z NonNegativeInteger hasMaxmumRainfall (x, y) (y <= 1750) hasMinimumRainfall (x, z) (z >= 0) ) ↔
DryZone(x))
This definition is represented in Protégé implementation (see below):
(b) Class Definition
The instances of the Location concept are related as follows:
x,y,z Location: ( x belongsTo y and y belongsTo z) x belongsTo z
For example, if Galle belongs to Wet Zone and Wet Zone belongs to Low country then the Galle belongs to Low country.
This semantic can be represented in OWL:
(c) Transitive Property
Fig. 3 Representation of different constraints
Representation of EnviornmentalFactor:
Union of a set of mutually-disjoint classes
(exhaustive partition)
Variety is a Crop
Semantic Representation
of User Location
Based on the existing information the additional
knowledge can be inferred. For example;
if Crop has GrowingProblemEvent and
GrowingProblemEvent has GrowingProblem then can
infer the Crop is affected by this Growing Problems
(object property chain in Protégé was used to represent
this):
hasGrowingProblemEvent o hasGrowingProblem
isAffectedBy
if GrowingProblem is GrowingProblem of
GrowingProblemEvent and GrowingProblemEvent has
related ControlMethod then can infer the
GrowingProblem is controlled by this ControlMethod:
isGrowingProblemOf o hasRelatedControlMethod
isControlledBy
(d) Composition of relations
Fig. 4 shows the Fertilizer Event represented using Cmap
tool. The Cmap (Concept Map) tool is used to view the
graphical representation of the ontology for better user
understanding [18]. The details of modeling the events
associated with second and third stages of the farming life
cycle and the associated challenges are explained in [10].
The implemented ontology using protégé is available at
http://www.sln4mop.org/ontologies/2014/SLN_Ontology. It
consists of 90 concepts, 205 object properties, and 45 data
properties. Currently it has 23 vegetable crops, 10 fertilizers,
19 growing problems, and 30 control methods. The more
details of the ontology development are explained in [11].
Fig. 4 FertilizerEvent Concept
D. Generalizing Approach
We have generalized the specific approach that was
developed to create the user centered ontology for Social Life
Networks. The Fig. 5 shows this generalized approach.
According to this approach, we first identify a set of
questions (Users’ Information Needs) that reflect various
motivation scenarios. Next we create a model to represent
information in user context. Then we derive the
contextualized information incorporating user context and
task modeling with generic knowledge module. We refer to
this contextualized information (refer Table III.) as the
informal CQs. These CQs are used to identify the ontology
components according to the Grüninger and Fox’s
methodology to develop the ontology.
Using this framework, we can extend the ontology for
different scenario problems. For example, when answering
scenario question like “How to control the growing problems
such as diseases, weeds, or pests in environmentally safe
manner?” we need to take into account suitable criteria for
selecting control methods and the users’ context with respect
to each criterion factor. We can then formulate the
contextualized information based on this systematic approach.
These questions drive the development of the ontology. By
doing so the contextual information/knowledge can be
represented by satisfying the user needs.
Fig. 5 Ontology Design Framework
Domain Knowledge from Reliable Knowledge Sources and
Outcomes of Interviews with Domain Users and Domain Experts
Users’ Information Needs
(Formally Referred to as Motivation Scenarios)
Generic Crop
Knowledge (in Agriculture Domain)
User Context model Task Criteria Modeling
Contextualized (or Personalized) Information
(Formally Referred to as Competency Questions)
Ontolog
y
Classification Axioms
Main Ontology Components
Specialization and Generalization
7
E. Validation and Evaluation Process
It is very important to check the validity of the ontology.
In this study, the correctness of the contents and correctness
of the construction of the ontology have been validated.
The content correctness depends on definitions of concepts,
relationships between concepts, hierarchical structures,
concept properties, and information constraints of the
ontology. The Delphi Method is a research technique that is
used to obtain the responses to a problem from a group of
domain experts [20]. We selected the Delphi method to
obtain expert advice and responses to check the definitions of
concepts, relationships, and data properties; and hierarchical
structures. The validation process is mainly done by
agricultural experts from different agricultural institute using
questionnaires base on the Delphi method. They verify the
correctness, relevancy, and consistency of the ontology
components and a set of predefined criteria. The modified
Delphi method can be adapted to use in face-to-face group
meetings, allowing group discussions [21]. Since we need to
make more dialogues and collaboration among the
participants in the Delphi group we arranged a discussion
based on the modified Delphi method. For this discussion
eleven (11) Agricultural Instructors (AIs) gathered at Lunama
Govi Jana Seva Center, Ambalanthota. The main aim of the
discussion was to check the criteria relevant to the fertilizer
application, growing problems and control methods, etc. The
Delphi investigator (one of the authors of this paper)
explained the problems in details to get experts’ knowledge.
Investigator also allowed them to discuss the problems and
possible solutions. Based on their responses, comments, and
suggestions we make judgments for the design criteria and
assumptions we made during the design process. The
contents of the ontology have been refined based on domain
experts’ feedbacks and comments.
One approach for checking the correctness of the
construction is to analyze whether the ontology contain
anomalies or pitfalls [22]. We first identified the common
pitfalls before the implementation. Next we identified the
types of Ontology Design Patterns (ODPs) that helps to avoid
the pitfalls by means of adapting or combining existing ODPs
[22]. Design patterns are shared guidelines that help to solve
design problems, for example Semantic Web Best Practices
and Development under W3C [23]. We also used the web-
based tool called OOPS! [22] to detect potential pitfalls in the
ontology. Using above methods we validated the ontology in
terms of accuracy and quality.
The implemented ontology using protégé is used to
evaluate the ontological commitments internally and also
used to test the consistency and inferences using reasoners.
We used the CQs to evaluate the ontological commitments to
see whether the ontology meets the users’ requirements using
Description Logic (DL) queries and SPARQL queries [11].
Next we checked the user satisfaction to the
information/knowledge in the ontology. We used a mobile
based application for this evaluation. A Mobile based
application was developed to provide information by using
this ontology [24]. The first evaluation was done only for
crop selection with a group of 32 farmers in Sri Lanka [24].
We have gathered suggestions from farmers and other
stakeholders of the domain for our future designs.
The Knowledge Base based on the ontology was created
by populating the ontology with instances to share and reuse
the agricultural information via the Web [11]. The online
knowledge base can also be used for evaluation process. We
can query the contextualized information on the Web via this
application (SPARQL endpoint) using SPARQL queries
(refer http://webe2.scem.uws.edu.au/arc2/select.php). This
application specially is useful for agricultural instructors,
researchers, and people at the Department of Agriculture to
find information based on their needs. For example, the
following SPARQL query lists the suitable environmentally
safe control methods to control Bacterial wilt disease for
Brinjal crop? We evaluated the knowledge represented in the
ontology by evaluating outputs of the queries. The output of
the following query is shown in Fig. 6.
PREFIX sln:
SELECT DISTINCT ?ControlMethods WHERE {
{?p sln:isControlMethodEventOfCrop
sln:Brinjal. }
{ ?p sln:hasRelatedGrowingProblem
sln:Bacterial_Wilt .}
{ ?ControlMethods
sln:isControlMethodOf ?p }
{ ?ControlMethods
sln:hasControlMethodType
"Cultural"^^xsd:string }
}
LIMIT 250
ControlMethods
Use_resistance_varieties_for_Bacterial_wilt
Deep_drain_to_facilitate_drainage
Crop_rotation_with_non_solanaceos_crops
Fig. 6 The output of the above query
III. ONTOLOGY MANAGEMENT SYSTEM (OMS)
If a developed ontology is not up-to-date or the annotation
of knowledge resources is inconsistent, redundant or
incomplete, then the reliability, accuracy, and effectiveness
of the ontology based systems decrease significantly [25].
Ontology building is a significant challenge for a number of
reasons, for example it takes a considerable amount of time
and effort to construct an ontology, it requires a sophisticated
understanding of the subject domain, and also it is even
greater challenge if the ontology developer or engineer is not
familiar with the domain of interest. Due to the increase in
volume of information, capturing the information,
maintaining it and making it usable is a challenge. Therefore
it is very important to be able to practically maintain a
developed ontology by updating the content of the ontology
in a timely manner, for example, extending the ontological
structure by improving coverage and modifying the instances
(individuals) in the knowledge base.
After developing the ontology we had to devise a method
to maintain it. A community based facility to manage the
structure of the developed ontology in the long term as well
as further populate the knowledge base is very useful. For
this we have developed an end-to-end semi-automatic
collaborative ontology management system for large-scale
development and maintenance purposes by giving facilities to
8
reuse, modify, extend, and prune the ontology components as
required. It also has facilities to capture users’ information
needs in their context, as well as search domain information
in user context. We use a web based application to deploy the
proposed framework. With the help of this web based
ontology management system, the people with little
knowledge about the ontology can help to modify the
ontology, and use the ontological information and knowledge
for their needs.
Fig. 7 A Framework for End-to-End Ontology Management System
The Fig. 7 shows the proposed framework for an end-to-
end ontology management system. The full details of the
design of the framework and development of the end-to-end
ontology management system based on the framework is
explained in [12]. In this paper we briefly present the
processes belonging to this framework. This framework
mainly has four processes such as Populate the ontology,
Modify the ontology, Search domain information in context,
and Capture users’ information needs and related users’
context for community based ontology development and
maintenance. This framework provides the essential facilities
to manage the ontology life cycle by supporting the identified
processes. Each process is briefly mentioned below.
A. Populate the Ontology
Using this process we can get the support from the
agriculture community to fully populate the knowledge base
in the long term. To populate, we specially get the
involvement of the people in the domain, for example,
domain experts such as agricultural instructors, information
specialist and researchers in agriculture community. To fully
populate the ontology with the real data, we develop a semi-
automated system to capture this information using web
based application. For that we have used a framework called
“CBEADs”: Component Based Ebusiness Application
Development and Deployment Shell [26] as a data capturing
application. This framework which is created using PHP and
MySQL has the potential to evolve with changing
requirements. More details related to each process can be
found in [12]. The Form as shown in Fig. 8 is used to gather
required data using the CBEAD application.
Fig. 8 Data gathering Interface for crop variety
9
B. Modify the Ontology
This process helps us to extend and prune the ontology
based on the changing and/or expanding user requirements
and related user contexts. This process can be performed by
agriculture domain experts and ontology developers. Since
the process to modify the structure of the ontology is
complex we need to mange this process carefully. This has
three processes; insertion, deletion, and updating (change).
Each process has three main activities. For example in
insertion process it needs to consider Inserting Concepts,
Inserting Data properties and Inserting Object properties. In
the same manner these activities can be seen in deletion and
updating processes. In this model, seven steps have been
proposed to modify the ontology such as view the ontology
structure (initial structure) represented in Cmap tool; extract
domain terms, concepts, and basic hierarchies using Text-To-
ONTO tool; view ontology design framework used to
represent the information in user context; based on the design
framework modify the structure using metadata (metadata
provides the information to users how to modify the structure
of the ontology, for example, how to insert the concepts, how
to delete the data properties or object properties, etc.);
validate the modified content using web forms; convert
modified content into RDF or OWL format; and finally
import modified content into initial ontology for information
integration. The way of modifying the ontology related to this
application is outside the scope of this paper and it is explain
in [12].
C. Search Domain Information in Context
To get the benefits from the knowledge base for all the
stakeholders in the community by finding the right
information based on their context we have included the
process “search domain information in context”. Through this
system we provide two facilities. Especially normal users
such as farmers can view the domain information in their
context and other stakeholders in the domain especially
agricultural instructors and researchers can retrieve domain
information and knowledge based on their interest. For the
farmers, we have provided specific answers to their questions
in their context using a natural language (in English, Sinhala,
and Tamil). Fig. 9 shows a user friendly interface for
searching information.
Fig. 9 Interface for searching information in context
D. Capture User Information Needs in Context
The process “capture the user information needs and
related user context” collects the information required to
extend the ontology further. Since to get the benefits to a
broad audience is even more challenging task, this
collaborative end-to-end ontology management system via
web based interface has now been expanded to include their
requirements in context. Then we can extend our ontology
with the different motivation scenarios that provide even
richer knowledge environment to support the agriculture
community.
Since this is a collaborative approach, the system mostly
relies on the users of the domain, their participation to the
system, and developers’ and administrators’ skills in
overseeing the collaborative processes. In our system (refer
http://webe2.scem.uws.edu.au/oms/index.php), there are three
main user categories (e.g. domain experts, normal users and
ontology developers) with different access rights. Fig. 10
shows the home page of the OMS (in English Language).
Fig. 10 Ontology Management System (OMS)
The domain experts and ontology developers need to be
logged-in to the system for populating and modifying the
ontology. Domain experts and ontology developers can
change or extend the ontology by getting the requirements
and user constraints from the system. There are processes to
capture user information needs and related user context from
the users to represent domain information in context. Domain
experts also involve populating the ontology by capturing
instance values through the forms. Through this system all
the stakeholders of the community can search information by
viewing user friendly interfaces (for the normal users such as
farmers) and/or querying the SPARQL endpoint in context
(for the advanced users). We have developed this web based
application in English but the English is not the official
language of Sri Lanka. Sri Lankan people mainly use their
native languages such as Sinhala and Tamil. We therefore
give the facility to use this application in their native
languages.
Fig. 11 shows the overall development process of a
community based ontology by summarizing above two
sections (II and III). This is an iterative process. Based on the
results and feedbacks of the validation and evaluation
processes the design of the ontology is refined using the
design framework shown in Fig. 5. Then the ontology can be
maintained using the web based ontology management
system based on the framework represented in Fig. 7.
10
Fig. 11 Overall Development Process of the Ontology
IV. CONCLUSIONS
Agriculture is the most important sector in Sri Lankan
economy. The people in agriculture domain in Sri Lanka
need agricultural information and relevant knowledge to
make optimal decisions for successful farming and/or do
research for development of the agriculture sector and
enhancement of the farming industry. Since not having
agricultural knowledge repositories that can be easily
accessed by people in agriculture community within their
own context is a major problem, a user centric knowledge
environment has been developed as a solution.
Through this study, we first identified the user context
model related to the farmers in Sri Lanka. Next we developed
a logic based ontological approach to meet the information
needs to suite the identified context. We have achieved this
by modifying how contextualized information is formulated
in a well-established methodology.
This article presents a summary of the overall ontology
development process to organize domain knowledge by
meeting particular access requirements effectively using the
guidelines shown in Fig. 11. We validated the ontology in
terms of accuracy and quality by using Delphi and modified
Delphi methods; a web-based tool; and ODPs. We evaluated
the ontology against the user requirements by using mobile
based and web based applications. The online knowledge
base with a SPARQL end-point to share and reuse the
domain knowledge was created. To fully populate the
knowledge base as well as modify the ontology by extending
coverage of the domain we developed a semi-automatic end-
to-end ontology management system that help us to develop
and manage complex real-world application based ontologies
in the long term as a collaborative process. Therefore this
OMS is a community activity.
We received very valuable feedbacks from the domain
experts during the group discussions in the modified Delphi
method as well as from and the field trials. Based on these
feedbacks we are now refining our application.
ACKNOWLEDGMENT
We acknowledge the financial assistance provided to carry
out this research work by the HRD Program of the HETC
project of the Ministry of Higher Education, Sri Lanka
(RUH/O-Sci/N2) and the valuable assistance from other
researchers working on the Social Life Network project.
Assistance from the National Science Foundation
(NTRP/2012/FS/PG-01/P-02) to carry out the field visits is
also acknowledged. We would also like to convey our
gratitude for farmers who took their valuable time to share
their ideas to clearly identify the user information needs, and
also the agricultural experts from different institutes who
gave us valuable suggestions/feedbacks by taking part of the
Delphi method and modified Delphi method; and also other
activities in the ontology management system.
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