ONTOLOGY BASED FLEXIBLE QUERYING SYSTEM FOR FARMERS

1. ONTOLOGY BASED FLEXIBLEQUERYING SYSTEM FOR FARMERS Presented By: Neha Arora Monday,17 Sept,2012 GISE Advanced Research Lab, CSE Dept, IIT Bombay

2. INTRODUCTION • Farmers express their queries in natural language which are usually answered by human experts. • Purpose is to enable the system to understand the user query as exactly as expert does. • Farmers have many questions regarding the type of soil/climate, type of pests, diseases and activity timelines related to their crop. • Existing Agro Advisory Systems • aAQUA system by IIT Bombay • eSagu by IIIT Hyderabad • mKRISHI at TCS GISE Lab, IIT Bombay

3. PROBLEM STATEMENT • System which handle farmers query without the need of agro-expert handling them • Farmers posts his observation/query to the system • System advises him or provides information on query • System acquires knowledge as exhibited by an agro-expert • This knowledge is stored as knowledge models or ontologies • Purpose is to provide context-based knowledge driven advisory solution GISE Lab, IIT Bombay

4. KNOWLEDGE REPOSITORY • System contains knowledge as: - Ontology: schematic or intelligent view over information resources - Information in Database Farmer’s past activity records Current activity records of farmer Data for weather forecast GISE Lab, IIT Bombay

5. CROP ONTOLOGY • Stores complete knowledge about the cotton crop • Information stored as classes, instances, properties and literal values • Ontology contains information of various areas - Variety - Disease - Pest - Symptoms - Control Measures - Activity (Fertilizing, Harvesting, Hoeing, Irrigation) GISE Lab, IIT Bombay

6. Generic/Specific Ontology • Generic Ontology - Ontology which defines classes and properties between them GISE Lab, IIT Bombay

7. Generic/Specific Ontology • Specific Ontology - Ontology which defines classes with their instances GISE Lab, IIT Bombay

8. Contextual ad Dependent Information • Annotation properties - associates information with classes and properties in ontology (*planning to use another way out) • In crop ontology, - Context Information: information dependent on context of crop (stage of crop and ongoing season) - Dependent Information: information giving related details of a class GISE Lab, IIT Bombay

9. Information In Database • Data collected from three districts of Punjab for past 5 years • It contains information regarding farmers and their farming practices • Current farming practices are also captured in the system ◮ Variety sown, date of sowing ◮ Irrigation details ◮ Fertilizer details - Nitrogen, Potassium, Phosphorous Application ◮ Spraying details - for Jassid, Whitefly, Tobacco Caterpiller • Data on weather forecast. And classification on that basis. GISE Lab, IIT Bombay

10. Ontology Based System • What it does? System has knowledge in both ontology and relational database - User queried information is searched over the ontology - Context information about the crop added from database filters this search GISE Lab, IIT Bombay

11. System Architecture GISE Lab, IIT Bombay

12. System Architecture Query Interface: • where farmer’s posts his observations about the crop or information which he seeks • Farmer enters a keyword based query Query Engine: • core part where query processing takes place • Understands user query, interprets and generates advice or information Database: • place where knowledge resides • Ontology is stored here along with farmer’s activity details and weather data GISE Lab, IIT Bombay

13. Ontology Querying • Overview of Algorithm GISE Lab, IIT Bombay

14. Farmer’s Query • State of farmer’s crop Date of sowing: 15th April Farming activity: Nitrogen Application on time Potassium Application on time Phosphorous Application on time Irrigation not on time • Query: Boll Shedding GISE Lab, IIT Bombay

15. Mapping Keywords over Ontology • First, exact match is searched • If not found, using WordNet find synonym matches • Keyword matches are found over the ontology Boll => {Boll, Boll Infection, Boll Shedding, Bad Boll Opening ...} Shedding => {Boll Shedding, Flower Shedding, Buds Shedding ...} • Final keyword matches Boll => {Boll, Boll Shedding} Shedding => {Boll Shedding, Flower Shedding, Buds Shedding ...} • Keywords classified as classes C, instances I, object property O, datatypeproperty D, literal L C => {} I => {Boll, Boll Shedding, Flower Shedding, Buds Shedding ...} O => {} D => {} L => {} GISE Lab, IIT Bombay

16. Find farmer’s activity Activity information stored in ontology as: GISE Lab, IIT Bombay

17. Find farmer’s activity • Check Activity Consistency Dates from Database Dates from Ontology 17 April 17 April 09 May 07 May 28 May 27 May 17 June 16 June Activity is fine. Next date for activity is recommended GISE Lab, IIT Bombay

18. Find farmer’s activity • Check Activity Consistency Dates from Database Dates from Ontology 17 April 17 April 09 May 07 May 28 May 27 May 17 June 16 June 27 June Excess of activity is performed GISE Lab, IIT Bombay

19. Find farmer’s activity • Check Activity Consistency Dates from Database Dates from Ontology 17 April 17 April 09 May 07 May 28 May 27 May ????? 16 June Lack of activity is found • Check stage at which activity is missed • If stage is critical, immediate action to be taken information marked on ontology Else, farmer is advised to perform activity • If lack of activity, add ontology instances to set activityI activityI => {Lack of Irrigation} GISE Lab, IIT Bombay

20. Find Context Information • Information which is based on crop context • Classes are associated with :context property in ontology • Context checked for stage and ongoing season - stage: whether the stage of class instance is same as of farmer crop - ongoing season: whether the class instance has a current period of existence • Find :context property for classes of instance set I, activityI and store in set contextI contextI=> {Spotted Bollworm, Bacterial Blight, Tirak, ... } GISE Lab, IIT Bombay

21. Find Dependent Information • Related information which we would like to display to the user • Eg: For a Disease queried by user, its Symptom and Control Measures would be of interest to him • Dependent information stored as :dependentClass property for classes in ontology • Find :dependentClass property for classes of instance set I, activityI, contextI and store in set dependentC dependentC => {Damage, Symptom} GISE Lab, IIT Bombay

22. Searching On Graph • Search is performed on graph, returning paths connecting the selected nodes Figure: Specific Ontology GISE Lab, IIT Bombay

23. Searching On Graph • Search is performed on graph, returning paths connecting the selected nodes Figure: Generic Ontology GISE Lab, IIT Bombay

24. Calculate Paths • Generic ontology has nodes as classes and edges as properties • Classes of instance sets I, activityI and contextI along with classes in dependentC and C are added to set SN of Selected Nodes • Find all possible paths between nodes in SN using BFS SN => {Damage, Part, Control Measure, Factor, Pest, Disease, Symptom} P => {Damage is_Caused_ByPest, Damage is_Caused_ByFactor may_Lead_ToDisease, Symptom is_Symptom_OfDisease occurs_Due_To Factor is_Controlled_By Control Measure, ... } GISE Lab, IIT Bombay

25. Calculate Instance Paths • For the paths obtained from Generic ontology, instance paths are calculated on Specific ontology • Paths with known instances of classes are selected Instance paths P => {Boll_Sheddingis_Caused_By Lack_of_Irrigationmay_Lead_ToTirakis_Controlled_ByProper_Irrigation, Flower_Sheddingis_Caused_ByLack_of_Irrigation May_Lead_ToTirakis_Prevented_ByProper_Irrigation, Boll_Sheddingis_Caused_BySpotted_Bollworm, Boll_Sheddingis_Caused_ByLack_of_Irrigation May_Lead_ToTirakhas_SymptomBlack_Boll_Color, Spotted_Bollwormcauses Buds_Shedding, ... } GISE Lab, IIT Bombay

26. Ranking of Paths • Paths are ranked based on several factors – + Keyword similarity count ◮ Exact matches ◮ Synonym matches + Actionable information Information derived from activity and context + Dependent information Information obtained from :dependentClassproperty + Paths containing edges selected by user in sets O and D GISE Lab, IIT Bombay

27. Output to the User • Finally the user is presented with set of paths which best match his search • Highest ranked path is most relevent, but other matched path are also returned Instance paths P => {Boll_Sheddingis_Caused_By Lack_of_Irrigationmay_Lead_ToTirakis_Controlled_ByProper_Irrigation, Flower_Sheddingis_Caused_ByLack_of_Irrigation May_Lead_ToTirakis_Prevented_ByProper_Irrigation, Boll_Sheddingis_Caused_BySpotted_Bollworm, Boll_Sheddingis_Caused_ByLack_of_Irrigation May_Lead_ToTirakhas_SymptomBlack_Boll_Color, Spotted_Bollworm causes Buds_Shedding, ... } GISE Lab, IIT Bombay

28. Similar Queries by other Farmers • State of farmer’s crop ◮ Date of sowing: 15th April Farming activity: Nitrogen Application not on time P => {Boll_Sheddingis_Caused_By Nitrogen_Deficiencyis_Controlled_ByApplication_of_Urea, Boll_Sheddingis_Caused_BySpotted_Bollworm, Buds_Sheddingis_Caused_ByNitrogen_Deficiency Is_Controlled_ByApplication_of_Urea, Spotted_Bollwormcauses Buds_Shedding, Flower_Sheddingis_Caused_BySpotted_Bollworm, ... } GISE Lab, IIT Bombay

29. Similar Queries by other Farmer • State of farmer’s crop ◮ Date of sowing: 15th April Farming activity: Nitrogen Application not on time Irrigation not on time P => {Boll_Sheddingis_Caused_By Lack_of_Irrigationmay_Lead_ToTirakis_Controlled_ByProper_Irrigation, Boll_Sheddingis_Caused_ByNitrogen_Deficiency is_Controlled_ByApplicatio_of_Urea, Boll_Sheddingis_Caused_ByLack_of_Irrigation may_Lead_ToTirakhas_SymptomBlack_Boll_Color, Boll_Sheddingis_Caused_BySpotted_Bollworm, Flower_Sheddingis_Caused_ByLack_of_Irrigation may_Lead_ToTirakis_Prevented_ByProper_Irrigation, Buds_Sheddingis_Caused_ByNitrogen_Deficiency is_Controlled_ByApplication_of_Urea, ... }

30. Interface

31. Information on Weather Forecast • Weather data for next 5 days captured from meteorological department for the three districts of Punjab • Data is captured about rainfall, temparature and humidity • System uses the rainfall data to help suggesting the farmer GISE Lab, IIT Bombay

32. Example Queries • Input Query 1: Leaf Turning Black Result: {Black_Leaf_Coloris_Symptom_Of Bacterial_Blightis_Prevented_ByRelease_Trichogramma, Black_Leaf_Coloris_Symptom_OfBacterial_Blight Is_Controlled_ByBlitox, Black_Leaf_Coloris_Symptom_OfBacterial_Blight Is_Controlled_ByStreptocycline, Black_Boll_Coloris_Symptom_OfTirak Is_Controlled_ByGreen_Manuring, ... } GISE Lab, IIT Bombay

33. Example Queries • Input Query 2: Varities of Cotton Result: {Cottonhas_Variety PAU_626 H Cottonhas_Variety LD_694 Cottonhas_Variety RCH_308 Cottonhas_Variety LH_1556 Cottonhas_Variety MRC_6304 Cottonhas_Variety MRC_6301 ... } GISE Lab, IIT Bombay

34. Example Queries • Input Query 3: Round Patches on Leaf Result: {Leaf_Sheddingis_Caused_ByLeaf_Blight has_SymptomCircular_Leaf_Spot Yellow to Red_Leaf_Coloris_Caused_By Potassium_Deficiencymay_Lead_ToLeaf_Blight has_SymptomCircular_Leaf_Spot Circular_Leaf_Spotis_Symptom_OfLeaf_Blight is_Controlled_ByMancozeb Circular_Leaf_Spotis_Symptom_OfLeaf_Blight occurs_Due_ToPotassium_Deficiencyis_Controlled_By Application_of_Potassium ... } GISE Lab, IIT Bombay

35. Ontology Validation GISE Lab, IIT Bombay

36. Conclusion • Web-based interface is designed where farmer can post his query • Search over ontology is performed which is aided by farmer’s context information from database • Results are ranked and returned as set of paths to the user • Farmer is also advised about his farming activity based on weather predictions GISE Lab, IIT Bombay

37. Future Work • Past data of the farmer can be mined to generate rules which would assist in better farming of current crop. • Protégé does not support rule execution, so in order to execute rule we need rule engine. Jess Rule engine is one of them which can be easily integrated with Protégé. Taking an example: Pest(?x) ^ Interval(?y) ^ Cure(?z) ^ Has_Interval(?x,?y) ^ Has_Interval(?y,?z) →Action(?z) If a Pest that occurs in Season July and for July season the Cure is Ethion Spray then Ethion spraying will be done. • New observations seen by farmers may be recorded and this knowledge may be updated in the crop ontology. • Current context based search focuses on temporal aspect. It can be extended to support spatial locality. GISE Lab, IIT Bombay

38. GISE Lab, IIT Bombay