Designing a Farmer Centred Ontology for Social Life Network
Anusha Indika Walisadeera
, Gihan Nilendra Wikramanayake
and Athula Ginige
School of Computing, Engineering & Mathematics, University of Western Sydney, Penrith, Australia
University of Colombo, School of Computing, Colombo, Sri Lanka
Keywords: Agricultural Knowledge, Social Life Network, Knowledge Representation, Ontology, Ontology Design.
Abstract: Rapid adoption of mobile phones has vastly improved access to information. Yet finding the information
within the context in which information is required in a timely manner is a challenge. To investigate some
of the underlying farmer centric research challenges a large International Collaborative Research Project to
develop mobile based information systems for people in developing countries has been launched. One major
sub project is to develop a Social Life Network; a mobile based information system for farmers in Sri
Lanka. Lack of timely information with respect to their preferences and needs to support farming activities
is creating many problems for farmers in Sri Lanka. For instance, farmers need agricultural information
within the context of location of their farm land, their economic condition, their interest and beliefs, and
available agricultural equipment. As a part of this project we investigated how we can create a knowledge
repository of agricultural information to respond to user queries taking into account the context in which the
information is needed. Because of the complex nature of the relationships among various concepts we
selected an ontological approach that supports first order logic to create the knowledge repository. We first
identified set of questions that reflect various motivation scenarios. Next we created a model to represent
user context. Then we developed a novel approach to derive the competency questions incorporating user
context. These competency questions were used to identify the concepts, relationships and axioms to
develop the ontology. Initial system was trialled with a group of farmers in Sri Lanka. There was universal
agreement among the farmers participated in the field trial to varying degree (strongly agree, agree,
moderately agree) to the question “All information for the crop selection stage is provided”.
From time to time farmers need information such as
seasonal weather, best cultivars and seeds, fertilizes,
information on pest and diseases, control methods,
harvesting and post harvesting methods, accurate
market prices, and current supply and demand to
make informed decisions at various stages of the
farming lifecycle (De Silva et al., 2012; Lokanathan
and Kapugama, 2012). Some of this information is
available from government websites, leaflets and
mass media. Farmers require this information within
the context of their specific needs. Such information
could make a greater impact on their decision-
making process (Glendenning et al., 2010).
Not having an agricultural knowledge repository
that can be easily accessed by farmers within their
own context is a major problem.
Social Life Networks for the Middle of the
Pyramid ( is an International
Collaborative research project aiming to develop
mobile based information system to support
livelihood activities of people in developing
The research work presented in this paper is part
of the Social Life Network project, aiming to
provide information to farmers based on their
context. For this we had to develop a knowledge
repository. Because of the complex nature of the
relationships among various concepts we selected an
ontological approach to create the knowledge
To represent the information in context-specific
manner, firstly, we need to identify farmers’ context
related to this application. We identified the context
specific to the farmers in Sri Lanka (i.e. farmers’
context) by analyzing information gathered from
various reliable knowledge sources. The Table 1
shows the farmers’ context that was identified
related to this application. The way of modeling the
farmers’ context and the farming stages related to
Indika Walisadeera A., Nilendra Wikramanayake G. and Ginige A..
Designing a Farmer Centred Ontology for Social Life Network.
DOI: 10.5220/0004495802380247
In Proceedings of the 2nd International Conference on Data Technologies and Applications (DATA-2013), pages 238-247
ISBN: 978-989-8565-67-9
2013 SCITEPRESS (Science and Technology Publications, Lda.)
this application is outside the scope of this paper and
it is explain in (Walisadeera et al., 2013).
Table 1: Farmers’ Context.
Farmers’ Context Description
Types of farmers
Preferences of
Farming stages
Information about environment
based on location of farm such
as elevation, rainfall, climate
zone, temperature, humidity,
sunlight, wind, soil, etc.
Farmers are classified based on
size of the cultivated farm land
and estimated budget for
cultivation. There are two main
categories; garden farmers and
commercial farmers.
Commercial farmers can be
further categorized as small-
scale farmers, medium-scale
farmers and large-scale farmers.
Farmers have their own
preferences such as high
yielding varieties, preferred
control methods and fertilizers,
low labour cost crops, high
disease and insect resistance
crops, desired farming systems
and techniques, etc.
Required information varies
based on different stages of the
farming lifecycle. To improve
overall decision making in
farming, we have defined six
farming stages covering all
required information needed by
farmers (refer Table 2 for
farming stages).
Recently, ontologies have emerged as a major
research topic in Information Systems. The term
‘ontology’ originated from philosophy and it is
concerned with the study of being or existence
(Rumbaugh et al., 1991). Lately, it has been used in
computer science and information science, for
knowledge engineering, databases and software
engineering purposes to define models that specify
reusable components and the relationships among
them. Ontologies are widely used for different
purposes (e.g. natural language processing,
knowledge management, e-commerce, intelligent
integration of information and semantic web) in
different communities (e.g. knowledge engineering
and databases).
An Ontology provides a structured view of
domain knowledge and act as a repository of
Table 2: Farming Stages.
Farming Stages Description
Crop Selection
Most important decision is
deciding which crops to grow.
Crop selection is a complex
process because it depends on
many factors. The environmental
factors mostly affected this
selection. Features of a crop,
farmer preferences, available
resources and market demand
are other key determinant for
this decision.
Refers to preparing the field for
selected crops. At this stage
farmer needs information on
quality agricultural inputs such
as seed rate, plant nutrients and
fertilizing, irrigation facilities
and new techniques for field
Includes information related to
managing the crop through its
growing stages. Information on
planting methods, good
agriculture practices (traditional
and new technology), common
growing problems and their
management is required in this
At this stage farmer needs
information related to harvesting
such as maturity time, methods
and techniques of harvesting,
expected average yield, labour
cost and total production cost for
Refers to proper handling after
harvesting. Required
information includes post
harvesting issues and
management, packing, grading,
storing, standardization,
transportation and value added
Refers to preparation for selling.
Mainly includes information
related to the market such as
market prices, consumer
behaviour and demand, and
alternative marketing channels.
concepts in the domain. This structured view is
essential to facilitate knowledge sharing, knowledge
aggregation, information retrieval and question
answering (Gruber, 1995). In addition, ontology
provides the means of deduction capabilities
provided by an inference mechanism and reasoning
support in order to generate further knowledge (i.e.
not explicitly known but can be deduced based on
the existing knowledge of the domain) (Fox et al.,
1996). Thus, ontology represents a better data model
(richer knowledge) than a normal data model.
Therefore, ontology can be used to find a response
to queries within a specified context in the domain
of agriculture. The most quoted definition of
ontology was proposed by Thomas Gruber as “an
ontology is an explicit specification of a
conceptualization” (Gruber, 1993). This definition is
adapted for our ontology.
The existing ontologies in the domain of
agriculture for example Thai Rice Ontology
(Thunkijjanukij, 2009) are crop-specific thus too
general and not specific enough to satisfy the
farmers’ needs for timely information in context.
To develop an ontology we need to carefully
identify suitable ontology development methodology
because there are several methodologies and
techniques for building ontologies reported in the
literature (Fernández-López and Gómez-Pérez,
2002). We select a first-order logic based approach;
Grüninger and Fox’s methodology (Grüninger and
Fox, 1995), to develop our ontology because its
expressiveness helps us to represent information in
context. Furthermore, this methodology provides a
formal approach to design ontology as well as a
framework for evaluating the adequacy of the
developed ontology. Its main strength is high degree
of formality and focuses on building ontology based
on First-Order Logic (FOL) by providing strong
Since there is no technique for formulating the
competency questions incorporating user context,
we have an issue with regarding to correctness of the
contents of the ontology. Therefore, the main
contribution of this paper is a novel approach to
derive the competency questions incorporating user
context. We also introduce a framework for
ontology design that we developed to design the
ontology for farmers to represent the necessary
agricultural information and knowledge within the
farmers’ context.
The rest of the paper is organized as follows.
Section 2 describes the design of the ontology for
the crop selection stage. This design process, also
includes a systematic approach to generate the
competency questions. In section 3 we present a
genaralisation of the approach that evolved from this
work. The section 4 provides a summary of initial
field trial used to test the ontology for crop selection
phase. In section 5 we conclude the paper with a
summary and describe the future direction.
In this section we describe the process that we used
to design the agricultural ontology for farmers. Our
ontology creation begins with the definition of a set
of farmers’ problems identified by reviewing related
literature in the domain of agriculture (Decoteau,
2000; Kawtrakul, 2012; Babu et al., 2012) and the
outcomes of the interviews with Sri Lankan farmers
and agricultural specialists. We generalize these
problems and organize these according to the
farming lifecycle stages (see Table 2). We take these
real farmers’ problems as a motivation scenario of
our application to provide information in context
(see Table 3).
Table 3: Real-World Application Scenario.
What are the suitable crops to grow?
What are the best 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 protect crops from disease?
Which are the most suitable control methods to a
particular disease?
What are the symptoms of a specific disease?
What are the most important factors to maintain
quality of harvested crops?
Which post-harvest method is best for a particular
What are the crops cultivated by other farmers and
in what amounts?
Next we identify areas of generic crop
knowledge required to answer these motivation
scenario questions. These broad areas of knowledge
we term as knowledge modules. The generic crop
knowledge consist of modules such as nursery
management, harvesting, post-harvesting, growing
problems, control methods, fertilizer, environmental
factors and basic characteristics of crops. A cultivar
(variety) is a group of crops that share common
qualities of crops of the same species (Decoteau,
2000). Each information module has related
information to answer the scenario questions. For
example, crop information module has information
about crops and cultivars. Next we identify
relationships among them. The following Figure 1
shows the generic crop knowledge module. To
represent agricultural knowledge within the farmer’s
context we need to associate this generic crop
knowledge with characteristics that describes the
farmers’ context.
Figure 1: Generic Crop Knowledge Module.
We begin our detail design process with first
question of the motivation scenario “What are the
suitable crops to grow?” Selecting proper crops and
cultivars is paramount for successful farming.
During initial interviews with vegetable farmers they
also identified this as a very important question.
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 farmer and
resources available for them for cultivation. We
have reviewed existing literature on crop selection
criteria to identify a suitable criterion which can be
used to assist farmers to make better decisions.
According to the Decoteau’s (2000), the crop
selection especially for vegetable crops depends on
four considerations; Crop Consideration, Farmer
Consideration, Labour Consideration and Marketing
Consideration. This criterion is designed only for
vegetable crops and for farmers in developed
countries. Therefore this criterion is not a good fit
for our application.
Bareja (2012) has identified following as major
crop selection factors for successful farming; Farm
conditions (an environmental scanning should be
conducted first), Crop or varieties adaptability (crops
and varieties should be selected based on
adaptability to farm conditions), Available
technology, Marketability and profitability,
Resistance to pests and diseases, Farming systems
and Security (crop selection may be done in favour
of security such as absence of security personnel).
This criterion is designed for multiple crops and
cultivars selection. However, it has not considered
the important factors such as labour cost and the
farmer types.
We next reviewed factors described in the
NAVAGOVIYA (CIC, 2012) web site which is one
of the important web sources in the domain of
agriculture in Sri Lanka for selecting suitable crops.
These factors are Climatic requirements, Soil
properties, Growing season, Labour availability and
cost, Raw material availability and Market demand.
According to the above analysis, environmental
condition has been identified as a most important
factor. Therefore, in our application, the
environmental conditions were given the first
priority. Also these conditions cannot be controlled
by the farmers. Next from the crops that meet the
environmental conditions farmers can choose the
best cultivars by considering factors such as high
yielding cultivars, the special characteristics of a
crop (e.g. colour, size, shape, flavour, hardiness,
nutritional quality, etc.), maturity and disease
resistance (Decoteau, 2000). These factors come
under farmer consideration because farmer can
decide importance of each of the factors according
to their interests. Based on various crop selection
criteria reviewed earlier we can see that only a few
preferences have been considered. However, in our
application we have included wide range of
preferences because this will help farmers to make
better decisions.
According to the collected information through
interviews with farmers and agricultural specialist in
Sri Lanka, information about what other farmers
grow in different regions and its quantities is also an
important factor when selecting crops because from
this information farmers can get an idea about
whether there is going to be an oversupply or not.
Farmers also consider the market information when
selecting crops; therefore, we take the market
information as a final consideration of our crop
selection criteria.
Finally farmers select the suitable crops and
cultivars by considering all the necessary factors
according to their own context. The following Table
4 provides a summary of the crop selection criteria
reviewed earlier.
We have defined a crop selection module (see
Figure 2) based on our crop selection criteria to
deliver information and knowledge related to crop
selection stage based on information needs of the
farmers. Environment, Crop, Cultivar and Basic
Characteristics are the same information modules
Growing Problems
Growing Practices
Control Methods
Post Harvesting
Table 4: Summary of Crop Selection Criteria.
Different Sources
Farmer types
Other farmers’
 
identified in generic crop knowledge module. The
information modules such as Farmer, Farmer Types,
Preferences and Market are additional information
modules needed for the crop selection.
Figure 2: Crop Selection Module.
In this module, we can notice that the farmer is
the central concept and this very much motivated us
in selecting a farmer centred approach to develop
our ontology. Since a cultivar (variety) is a group of
crops that share common qualities, we have
identified a cultivar as a subset of a crop (i.e. a
cultivar is a crop). Types of farmers are classified
based on size of the cultivated farm land and
estimated budget for cultivation. In here
environment is designed with regard to farm
environment and crop environment. In this stage we
do not consider market information as a factor of
crop selection because our initial efforts have
focused only on the static information. Static
information represents data that rarely change over
time while dynamic information such as market
prices changes frequently and hard to obtain without
an elaborate network to gather market data in real
The next step is formulation of a set of informal
competency questions based on the motivation
scenarios. Competency questions determine the
scope of the ontology and use to identify the
contents of the ontology. The ontology should be
able to represent the competency questions using its
terminologies, axioms and definitions. Then,
knowledge-base based on the ontology should be
able to answer these questions (Grüninger and Fox,
1995). These questions are benchmarks in the sense
that the ontology is necessary and sufficient to
satisfy the requirements specified by the competency
questions (Fox et al., 1996). Therefore, formulation
of the competency questions is a very important step
because these questions guide the development of
the ontology. Also as indicated in Fernández-López
(1999), the techniques for formulating the
competency questions are not mentioned in the
Grüninger and Fox’s methodology. We therefore
have reviewed the literature to search whether there
is a method to formulate the competency questions
of an ontology.
Fernandes, Guizzardi and Guizzzardi (2011)
have suggested to apply the Tropos methodology
(Bresciani et al., 2004) to formulate the competency
questions. Tropos is an agent-oriented software
engineering methodology. According to the
proposed approach competency questions are
defined to accomplish the goals of the actor.
Systematic analysis of the goals leads to the
understanding what are the objectives involved in an
organizational environment. To satisfy these goals,
the right kind of information is needed, thus, the
competency questions are defined based on this
information need. Since proposed approach is agent-
oriented, it deals with establishing the needs of
stakeholders that are to be solved by software.
Therefore, this approach is not suitable for our
application to model the competency questions.
Thus we had to develop our own approach to
formulate the competency questions. With the help
of the domain experts we first identified the breadth
of information required by farmers. Next based on
earlier identified user context what conditions we
can use to obtain a subset of information that can
satisfy a specific information need of users. Then,
we generate the competency questions on the above
basis as follows:
covering all information of the farming stages
(e.g. crop selection to selling stage) and its
constraints (restrictions) – it represents the
knowledge needed by the farmers
farmers’ conditions based on the farmers’
context (e.g. farm environment, farmer types,
farmer preferences and farming stages) – it
provides information in context.
As an example, the Figure 3 shows our basis for
formulating competency questions for crop selection
Farmer Types
Figure 3: Basis of Modelling Competency Questions.
On this basis, formulation of competency
questions for crop selection depends on multiple
criteria such as the farmers’ context, general crop
knowledge, crop selection criteria and the farmers’
constraints. This serves as a basis for formulating the
competency questions in a user context because it
satisfies the expressiveness and reasoning
requirements of the ontology. Some examples for
competency questions related to each category of
crop selection are given in Table 5.
Table 5: Competency Questions for Crop Selection Stage.
Suitable crops based on the Environment:
Which crops are suitable to grow in the
‘LowCountryDryZone’ agro-ecology region?
Which crop’s cultivars are the most appropriate
for ‘WetZone’ and ‘Maha’ season?
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 Farmers:
What are the crops involving in high labour cost?
What Brinjal’s cultivars are good for the ‘Bacterial
Wilt’ disease?
What are the crops with high resistance to a
Suitable crops based on Farmer related Information:
Which crops have been cultivated by vegetable
farmers and which quantities?
What is the expected average yield of each
How many farmers are involving in vegetable
farming in ‘UpCountryWetZone’ zone and ‘Maha’
Suitable crops based on Environment, Preferences and
Other Information:
What is the best Brinjal’s cultivar which is
suitable for ‘DryZone’ and high-resistance to the
‘Bacterial Wilt’ disease?
Note that, when we are providing specific answers to
the questions, additional information related to the
questions can be provided to the farmers. For
example necessary environmental conditions which
are relevant to crops, because this additional
information will also affect their decision-making
process (especially crop selection, applying control
methods and fertilizing).
In order to answer these competency questions,
we need to identify the ontology components. There
are three main ontology components; concepts,
relationships and constraints. Concepts are classes,
entities, sets, collections that represent ideas about
physical or abstract concepts that constitute a
domain. Relationships specify the interaction among
concepts. Constraints capture additional knowledge
about the domain and it can be represented as
axioms (logical expressions that are always true).
In our ontology design, we use the middle-out
strategy to identify the main concepts (Uschold and
Gruninger, 1996). The main advantage of this
approach is that it starts with most important
concepts first. Once the higher level concepts are
defined the specialized and generalized hierarchies
get identified. Thus, these concepts are more likely
to be stable. This results in less re-work and less
overall effort.
There are few concepts which we can directly
elicit, for instance, we have identified Farmer as a
main concept of our ontology based on scope of the
ontology (i.e. represent agricultural knowledge for
farmers). We also identified Crop as a major concept
of this application. Next, we need to identify other
major concepts by analysing each competency
For example: Which crops are suitable to grow
in the ‘LowCountryDryZone’ agro-ecology region?
The main concept of this query is Zone. Then we
need to define specialized and generalized (if
necessary) hierarchies based on the following
concept properties,
nature of the instances (instances are used for
denoting specific members of a concept and
represented by constants or variables),
generic crop knowledge structure, and
the farmers’ context.
To represent the information in context we need
to identify the details of each concept in multiple
levels. Therefore, designing this type of ontology is
an extremely complex task. It needs to be done in a
very systematic way.
The concept Zone has properties such as
maximum rainfall and minimum rainfall. By
Crop Knowledge
Selection Criteria
Competency Questions
Information Modules
specializing Zone concept we have defined the
concept AgroZone (agro-ecology zone) as a subclass
of Zone, because there are several additional
properties specific to AgroZone such as maximum
and minimum temperature, and maximum and
minimum elevation. The properties of concept Zone
can be inherited by the AgroZone concept because
of the taxonomic hierarchy (is_a). AgroZone is a
subclass of Zone if and only if every instance of
AgroZone is also an instance of Zone.
Based on the definition of concept Zone (see
Table 8), we can categorize the instances as
DryZone, IntermediateZone and WetZone if there is
no further categorization (has only first level
categorization). To reduce complexity of the design
of the ontology, we restrict first level categorization
as a property of a concept (e.g. ConceptType). Then
Zone concept has three properties such as
ZoneType, maximum and minimum rainfall. In the
same manner, crops can be categorized based on the
types of crops such as vegetable, fruit, spices, onion,
chilly and a number of other tuber crops, grains, etc.
We define CropType as a property of a Crop. The
Table 6 shows few concepts and their properties
related to the crop selection stage.
Table 6: Concepts, Sub-concepts and Properties related to
Crop Selection.
ZoneType, MinimumRainfall,
MaximumElevation, MinimumElevation,
MaximumTemperature, MinimumTemperature
ElevationType, MaximumElevation,
StartMonth, EndMonth
PhValue, Moisture, Nutrition, Texture,
Drainage, EdaphicProblem
CropType, Hardiness, Nutrition,
Length, Colour, Shape, Flavour, Size, Quality,
Weight, DiseaseResistance,
DiseaseResistanceRate, DroughtResistance,
SizeOfFarmLand, BudgetForCultivation,
FertilizerType, TimeOfApplication, Source,
ApplicationMethod, Quantity, Cost
MethodType, ApplicationMethod, Source,
TimeOfApplication, Quantity, Cost
In farmer-centric view, farmers need to retrieve
the agricultural information with respect to their
preferences, needs and their situation. Here farmer
can be grouped into two main categories such as
garden farmers and commercial farmers based on
size of the cultivated farm land and estimated budget
for cultivation. Commercial farmers can be further
categorised as small-scale farmers, medium-scale
farmers and large-scale farmers. Since there is a
more than one level categorisation, we have
organised this categorisation as a taxonomic
organisation (e.g. is_a relation not as a
The associative relationships (non-taxonomic)
are specified as follows:
identify the concepts and relationships with
meaningful relations,
define the relationships and its inverse
relationships (if applicable).
For example, there is an associative relationship
with inverse relationship between Crop and Cultivar:
Crop hasCultivar Cultivar, Cultivar isCultivarOf
Crop (see Figure 4). The Table 7 shows some
relationships including associative relationships with
Table 7: Associative Relationships with Inverse.
hasCultivar, isCultivarOf
isAffectedBy , affects
isCausedBy, causes
isControlledBy, controls
cultivates, isCultivatedBy
Based on our analysis, Crop has main properties
such as Crop Type, Hardiness, Nutrition and Special
Characteristics. Since Cultivar is a subclass of Crop
these properties can be inherited. Other than these
properties Cultivars has properties such as Length,
Colour, Shape, Flavour, Size, Quality, Weight,
Disease Resistance and its Resistance Rate, and
Drought Resistance and its Rate. We have paid
special attention to properties specific to Cultivars
because, when selecting crops the farmers primarily
consider basic features of a Cultivar not a Crop.
Figure 4: Relationships about Environment Concept.
hasFarmEnvironment dependsOn isCultivarOf
is-a is-a is-a is-a
Crop Farme
Figure 4 represents how EnvironmentalFactor
relates to Farmer and Crop; hasFarmEnviornment
and dependsOn respectively. We have defined the
environmental factors to be Zone, Elevation, Soil,
Temperature, etc. which are subclasses of the
EnvironmentalFactor (superclass) because in our
application the union of these subclasses form the
environmental factors which need to be specified for
instances of crops as well as for farms. Since
AgroZone is a subclass of Zone, then AgroZone is
also subclass of EnvironmentalFactor. In here we
have used the subclasses as a set of mutually-disjoint
classes which covers EnvironmentalFactor. Every
instance of EnvironmentalFactor is an instance of
exactly one of the subclasses in the union.
Once the hierarchies and relationships have been
identified, the next step is to define the informal
competency questions (CQ) in a formal way using
formal terminologies. Few examples are given
Note that, here we use unary predicates for
representing concepts, binary predicates for
properties and binary relationships. The
interpretation of C(x) is that x is individual belongs
to concept C.
CQ: Which crops are suitable to grow in the
‘LowCountryDryZone’ agro-ecology region?
Query 1:
(AgroZone(LowCountryDryZone)) dependsOn(x,
CQ: What Brinjal’s cultivars are good for
‘Bacterial Wilt’ disease?
Query 2:
x) (Cultivar(x)) Crop(Brinjal) 
hasCultivar(Brinjal,x) isCultivarOf(x,Brinjal) 
CQ: What is the best Brinjal’s cultivar which is
suitable for ‘DryZone’ and high-resistance to the
‘Bacterial Wilt’ disease?
Query 3:
x) (Cultivar(x)) (Crop(Brinjal))
isCultivarOf(x,Brinjal) hasCultivar(Brinjal,x) 
hasDiseaseResistanceRate(x,high) (Zone(z))
dependsOn(x, z) hasZoneType(z,DryZone);
To perform above queries, definitions of the
terms and constrains in their interpretation are
specified using set of axioms in first-order logic.
Here we have defined the axioms to express these
definitions and constraints. The Table 8 shows some
of the axioms used to represent above queries.
Table 8: Formal Axioms.
Express main climate zones in Sri Lanka based on
annual rainfall (in mm):
x (Zone(x) (y Integer(y)
avgAnnualRainfall(x,y) (y<1750))
x (Zone(x) (y Integer(y)
avgAnnualRainfall(x,y) (1750
x (Zone(x) (y Integer(y)
avgAnnualRainfall(x,y) (2500<y))
Express main farmer types based on his/her cultivated
land area:
x (Farmer(x )(y Integer(y)sizeOf
FarmLand(x,y)(y<35)) SmallFarmer(x) ˅
GardenFarmer(x) );
x(Farmer(x )(y Integer(y)
sizeOfFarmLand(x,y) (y>35) (z
Integer(z)maxWorkers(x,z) (z>12))
LargeFarmer(x) ˅ CommercialFarmer(x));
Express the planted land types according to elevation in
x(Elevation(x) (y Integer(y)
maxElevation(x,y) (y<600) LowLand(x) ˅
Pahatharata(x) ˅ LowCountry(x));
x(Elevation(x)(y Integer(y)
maxElevation(x,y)(y<1200)( z Integer(z)
minElevation(x,z)(600<z) MidLand(x) ˅
Medarata(x) ˅ MidCountry(x));
x (Elevation(x )(y Integer(y)
minElevation(x,y)(y>1200) HighLand(x) ˅
Udarata(x) ˅ UpCountry(x));
In this ontology, inference capability is to be
represented by using inheritance and the first-order
logic based axioms, i.e. it refers to the implicit
knowledge derived from the ontology when
reasoning procedures are applied to the ontology.
We have now generalised the specific approach that
was first developed to create a farmer centric
ontology for Social Life Networks. The Figure 5
shows the generalised approach. According to this
approach, we first identify a set of questions that
reflect various motivation scenarios. Next we create
a model to represent user context. Then derive the
competency questions incorporating user context
with generic knowledge module. This step is a new
contribution we have made in this paper. These
competency questions are used to identify the
concepts, relationships and axioms to develop the
Figure 5: Our Ontology Design Framework.
Figure 5: Our Ontology Design Framework.
Using this framework, we can extend the
ontology for different scenario problems. For
example, when answering scenario question like
Which are the most suitable control methods to a
particular disease?” we need to take into account
suitable criteria for selecting control methods and
farmers’ context. Then we can formulate the
competency questions based on this systematic
approach. These competency questions drive
development of the ontology and can represent
contextual information by satisfying user needs.
A Mobile based application was developed to
provide information to farmers using this ontology
(De Silva et al., 2013). The ontology that we
developed for crop selection phase was tested with a
group of 32 farmers in Sri Lanka. These farmers
were selected with the help of Agriculture extension
officers from Matale District in Sri Lanka. In this
district high percentages of people are involved in
cultivating wide range of vegetables. The evaluation
study comprised of a demonstration session where
farmers were given a brief introduction to the
research project and what is expected from them.
First a training session was carried out to make
farmers familiar with the touch screen technology.
Next the crop selection prototype shown in Figure 6
was demonstrated while illustrating the key features
incorporated into the application. Crop selection
provides a list of crops that grow in the region based
on farm location and farmer preferences.
Figure 6: List of Crops related to Crop Selection.
After farmers used the application to select a
crop that they want to grow they were asked the
question “Is all information for the crop selection
stage provided”. They recorded the answer on a 1 to
5 Likert scale; strongly agree, agree, moderately
agree, disagree, strongly disagree. The responses 7%
strongly agree, 57% agree and 36% moderately
agree. The farmers also suggested few areas where
they would like to get more information.
In this paper we have presented a novel approach to
derive the competency questions incorporating user
context. These competency questions were used to
identify the concepts, relationships and axioms to
develop the ontology.
Overall objective of this research project is to
design an ontology to cover all stages of farming
lifecycle and to provide agricultural information and
knowledge to framers in their own context.
Designing this type of ontology is not a simple task,
because it depends on many factors. In this paper we
have briefly explained how we designed the
ontology for the crop selection stage. For this we
had to extend the ontology to incorporate user
Motivation Scenario
Information/Knowledge on the Literature and
Outcomes of Interviews with Farmers/Experts
Generic Crop
Main Ontolo
Farmers’ Context Criteria
context. Based on the techniques that we discovered,
we developed a generalised framework for ontology
design that can be used to create knowledge
repositories which are capable of providing
information according to user context.
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.
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