User Centered Ontology for Sri Lankan Farmers

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1 User Centered Ontology for Sri Lankan Farmers Anusha Walisadeera School of Computing, Engineering and Mathematics University of Western Sydney Australia 1

2 Outline Problem Agrarian Crisis in Sri Lanka Root Cause and Aim A Problem Common to Many Domains Design Approach Generalizing the Approach Ontology Development Process Ontology in Practice Conclusions 2

3 Agrarian Crisis in Sri Lanka N Eliya carrot farmers in the dumps: Bumper harvest, but prices low The Sunday Times, April 22,

4 Root Cause Analysis

5 Aim To provide agricultural information and relevant knowledge that is Complete and Structured Specific to User Context (Major Contribution of this Research) 5

6 A Problem Common to Many Domains 6

7 Farmers 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 protect crops from diseases? Which are the most suitable control methods to a particular disease? What are the symptoms of a specific disease? What are the best techniques for harvesting? What are the important factors to maintain quality of harvested crops? Which post-harvest method is best for a particular crop? What are the crops cultivated by other farmers and in what quantities? 7

8 Farmers Context When responding to questions, response can vary from farmer to farmer: Farm environment: information about environment based on farm location Types of Farmer: classified based on cultivated land area and estimated budget for cultivation Preferences of farmer: farmers have their own preferences Farming stages: information varies based on different stages of the farming life cycle 8

9 Farming Stages Crop Selection Pre-Sowing Growing Harvesting Post Harvesting Selling 9

10 Design Approach Focuses on static information to represent knowledge Organize the farmers information needs according to the farming stages Identify Generic Crop Knowledge required to answer the farmers information needs Identify criteria for task modeling Examples: Criteria for Selecting Crops Criteria for Applying Fertilizers Criteria for Applying Pesticides 10

11 Basic Characteristics hascropcharacteristics Variety hasvariety contains uses Nursery Management Fertilizer Environmental Factors dependson Crop hasharvestinfor Harvesting involves isaffectedby haspostharvesttechniques Growing Practices Growing Problems Post-Harvesting iscontrolledby hassymptom Control Methods Symptoms Generic Crop Knowledge Module 11

12 Farmer Types Farmer Preferences hasfarmertype haspreferences Environmental Factors hasfarmenvironment dependson Farmer needs Market Information Basic Characteristics hascropcharacteristics Crop Variety Criteria for Selecting Crops Task Modeling 12

13 Generic Crop Knowledge Additional Knowledge Modules Farmer Context Task Modeling Criteria Contextualized (or Personalized) Information Farmers Information Needs in Context 13

14 Farmers Information Needs in Context Farmers Information Needs What are the suitable crops to grow? Which are the most suitable control methods to a particular disease? Farmers Information Needs in Context Suitable crops based on the Environment: What are the suitable vegetable crops for UpCountry, applicable to the Welldrained Loamy soil, and average rainfall > 2000 mm? Suitable control methods based on Preferences of Farmers: What are the suitable chemical control methods and in what quantities to control Damping-off for Tomato? Generalizing Contextualized Information What are the suitable Types of Crops for specified Location, applicable to the specified Soil types, characteristics, and other conditions? What are the suitable Types of control methods to specified growing problem of a Crop? 14

15 Why Ontology? Identify an optimum way to organize this information Methods to organize information Relational data models Web pages Ontology Provides structured view of domain knowledge and act as a repository of concepts in the domain Facilitates knowledge sharing and aggregation, information retrieval and question answering Represents a better data model than normal data model Use ontological approach to represent this information Data complexity of the relationships To attenuate the incompleteness of the data To add semantic and background knowledge about the domain 15

16 What is an Ontology? Definition: An ontology is an explicit specification of a conceptualization (Thomas Gruber,1993) 16

17 Example for Ontology in Healthcare x, y (sufferfrom (x, y) ill (x)) cure(doctor,disease) is_a(doctor,person) DISEASE, PERSON {disease, frda.h} disease, frda.h Axioms & Rules Relationships Taxonomy Concepts (Multilingual) Synonyms Terms 17

18 Ontology Development Select Grüninger and Fox s methodology Identify main a motivation scenario A set of Farmers information Needs Identify Competency Questions (CQs) Farmers Information Needs in Context Identify ontology components for each CQ Define specialized and generalized hierarchies axioms 18

19 Crop Life Cycle Events Ontology modeling is not a simple task A real-world scenario in the application domain Events are happening at different times at different locations E.g. Applying Fertilizer, Selecting Control Methods How to model events? To model this complex situation Need to introduce Events Events describe the related information for a given time and location (space) 19

20 Event based Modeling for Fertilizer To apply a fertilizer for a specific crop: For a given Crop in My Farm need to know fertilizer(s) quantity and its unit and other information Fertilizer quantity depends on many factors: Fertilizer types, location, water source, soil Ph range, time of application, application methods, etc. To represent all information related to a fertilizer quantity: Introduce a new concept as Fertilizer Event Define new relationships to describe this event 20

21 Variety Unit hasfeapplicationmethod(f) Crop isfertilizereventof(f) hasfertilizerunit(f) isfertilizerunitof uses isusedby hasfertilizerevent isfertilizerof FertilizerEvent Fertilizer hasfertilizer(f) hasfeminsoilph(f) Fertilizer Type Source MinPhValue isfeminsoilphof hasfemaxsoilph(f) isfemaxsoilphof MaxPhValue ApplicationMethod hasfetimeofapplication(f) isfetimeofapplicationof Notations: TimeOfApplication isfeapplicationmethodof isfelocationof hasfertilizerquantity(f) isfertilizerquantityof Quantity hasfewatersource(f) hasfelocation(f) hasfertilizerspinfor(f) isfertilizerspinforof Location isfewatersourceof SpecialInformation Water Source FertilizerEvent Concept 21

22 Example What are the suitable fertilizers and in what quantities for Banana which are grown in Dry Zone? Fertilizer ApplicationMe thod TimeOfApplica tion Quantity Unit mop.org/ontologie s/2014/sln_ontolo gy#urea mop.org/ontologie s/2014/sln_ontolo gy#triple_super_ph osphate(tsp) mop.org/ontologie s/2014/sln_ontolo gy#muriate_of_pot ash(mop) mop.org/ontologie s/2014/sln_ontolo gy#top_dressing_1 mop.org/ontologie s/2014/sln_ontolo gy#top_dressing_1 mop.org/ontologie s/2014/sln_ontolo gy#top_dressing_1 mop.org/ontologie s/2014/sln_ontolo gy#2_months_after _planting mop.org/ontologie s/2014/sln_ontolo gy#2_months_after _planting mop.org/ontologie s/2014/sln_ontolo gy#2_months_after _planting mop.org/ontologie s/2014/sln_ontolo gy#120 mop.org/ontologie s/2014/sln_ontolo gy#80 mop.org/ontologie s/2014/sln_ontolo gy#250 mop.org/ontologie s/2014/sln_ontolo gy#kilograms_per_ hectare mop.org/ontologie s/2014/sln_ontolo gy#kilograms_per_ hectare mop.org/ontologie s/2014/sln_ontolo gy#kilograms_per_ hectare 22

23 Event based Modeling of Control Methods Disease symptoms varies with different crop E.g. Tomato and Brinjal face same diseases such as Bacterial Wilt but their symptoms and recommended control methods are different based on crop Different causal agents and it occurs in different stages To represent this situation: Introduce a new concept as Growing Problem Event and relationships Growing problem event has different control methods Control method depends on: Control method types, soil type, location, time of application, application stage If control method is chemical it involves a quantity To identify suitable control methods with respective to Growing Problem Event: Introduce a new concept as Control Method Event 23

24 Cause Type Problem Stage Disease Disease Type Cause Symptom relatesagent isrelatedagentof CausalAgent hassymptom causes iscausedby(f) Crop hascausalagent(f) iscausalagentof hasgrowingproblemevent isgrowingproblemeventof(f) issymptomof GrowingProblemEvent hascontrolmethodevent iscontrolmethodeventof(f) isaffectedby affects hasgrowingproblem(f) isgrowingproblemof relatescm isrelatedcmof hascontrolmethod(f) iscontrolmethodof GrowingProblem iscontrolledby controls ControlMethod Method Type 24 GrowingProblemEvent and ControlMethodEvent Weed Pest DiseaseCM WeedCM PestCM hascmeunit(f) hascmquantity(f) Application Stage Unit ControlMethodEvent Quantity iscmquantityof iscmeunitof hascmapplicationmethod(f) haspesticide(f) ispesticideof iscmapplicationmetho iscmspinforof Pesticide Pesticide Type ApplicationMethod dof hascmlocation(f) hascmspinfor(f) iscmtimeofapplicationo f hascmsoiltype( Special Information hascmtimeofapplication(f) F) iscmsoiltypeo iscmlocationof f TimeOfApplication SoilType Location InsectPest NematodePest InsectPestCM NematodePestCM

25 RegionalArea belongsto Zone belongsto Elevation isbelonged isbelonged isbelonged belongsto belongsto isbelonged isbelonged belongsto isbelonged belongsto District belongsto isbelonged Province Inferred Knowledge RegionalArea belongsto Zone RegionalArea belongsto Province District belongsto Elevation Farmers Location 25

26 Domain Knowledge from Reliable Knowledge Sources and Outcomes of Interviews with 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) Main Ontology Components Specialization and Generalization Classification Axioms Ontology Generalizing the Approach 26

27 Resulting Ontology Main ontology components: 83 Concepts (Crop, Zone, Variety, ) 45 Data Properties (CropType, CropFamily, ) 191 Relationships (Crop growsin Location, Crop hasvariety Variety, ) Taxonomies (AgroZone is a subclass of Zone, Variety is subclass of Crop, ) Axioms (specify definitions of terms and constrains) $x,y,z Crop(x), FertilizerEvent(y), Fertilizer(z), (hasfertilizerevent (x, y) hasfertilizer(y, z) uses(x, z)) 27

28 Ontology Development Process Ontology Design Implementation: Language OWL, Tool Protégé Validation: Correctness of Content Delphi Method Correctness of Construction OOPS! Evaluation: Mobile based Application Online Knowledgebase (SPARQL endpoint) Maintenance: Web based Application 28

29 Capturing Instance values 29

30 Ontology in Practice 30

31 Ontology in Practice Cont... 31

32 Conclusions Present a model to represent information and knowledge in user context Validate, Evaluate, and Maintain the ontology Set-up a SPARQL end-point to share and reuse the domain knowledge Found using this approach, to add more concepts, relations and constraints to suit different scenarios Towards Social Ontology Management System 32

33 Questions?