Overview of Expert Systems

1. Overview of Expert Systems Sudeep Marwaha Division of Computer Applications, IASRI sudeep@iasri.res.in

2. Expert System of Extension Developed By : Indian Agriculture Research Institute & Indian Agricultural Statistics Research Institute

3. INTRODUCTION This Project is meant to provide required information and expert advice to the farmers and extension workers at Krishi Vigyan Kendra’ s according to their needs & available resources. For example: - • On the basis of symptoms supplied by the farmer, diseases affecting the crop can be detected • Which practices should be adopted according to the geographical locations or climate for a better yield, etc.

4. OBJECTIVES • To categorize agriculture in sub-areas & collect relevant information of these areas to feed into database • To make decision rules to process the information. • To design & develop the web based expert system in extension. • To provide required information to the farmers and extension workers to take decisions before starting the agricultural enterprise.

5. AgriDaksh Developed By : Division of Computer Applications Indian Agricultural Statistics Research Institute

6. Expert System of Maize • Collaborated with Directorate of Maize Research • DMR Scientists are domain experts • ESE is a base technology • Enhanced Features • System has a new look and personalized homepage • Credit is given to the scientist and institution for the added information at the individual entity level e.g. for each disease, insect, agronomic practice, variety etc. • Maize Products Module • More featured user/farmer feedback module • Enhanced Information Validation Control • Support for Audio and Video Files

7. Home Page

8. Technology • It is a Rule Based Expert System. • It is a Web based System. • It has Java Expert System Shell (JESS) as an alternative to AI Programming Language (like LISP, Prolog). • It is incremental or upgradeable in nature as it is built in Java. • Open to new technologies like Semantic Web.

9. METHODOLOGY This Project is mainly divided into 2 parts: - • Knowledge Acquisition & Formulation of Decision Rules i.e. Collection of Agricultural Information of some selected crops from authentic sources & their Storage in the Knowledge Base( as Facts & Rules). • Development of the Web Based User Interface.

10. KNOWLEDGE ACQUISITION • Selected Areas: ICAR-Agroclimatc Region 4, (Ludhiana, Karnal, Gurgaon, Hisar, Delhi, Anand, etc.) • Selected Crop: Paddy, Pea, Mustard, Tomato, Gladiolus, Mushroom, Mango. Continued...

11. KNOWLEDGE ACQUISITION Knowledge Acquisition Process Technical & Extension Bulletins Research Findings Data, Problems, Question Domain Expert Know--ledge Base Knowledge Engineer Knowledge, Concepts, Solutions Structured Knowledge Text Books Facts

12. Front End (through Web Browser) (made in HTML, Java Script) Knowledge Acquisition & Explanatory Interface 4 Application Logic Layer (Java Server Pages) Inference Engine Layer (Java Expert System Shell) 3 2 Knowledge Base (Database Layer: SQL Server) 1 ARCHITECTURE n Different Layers of Architectural Components

13. ARCHITECTURAL COMPONENTS 1 Knowledge Base contains Facts & Rules about some specialized knowledge domain (Example: Crop Diseases). Java Server Pages are used here. Server Side Scripting language meant to receive user’ s input, then processes it according to logic underneath & responds back to the user. 2 3 2

14. ARCHITECTURAL COMPONENTS 3 Inference Engine accepts User’s input Queries & Responses and uses this dynamic information with static knowledge present in the Knowledge base in form of facts & rules to derive a conclusion. Front End has been designed using HTML/DHTML and validations are put through using JavaScript. 4

15. Features • One System for all crops. • Ability to Add New Crops. • Location Specific Variety Information. • Ability to Define Knowledge Model for Crops Online. • Comprehensive Plant Protection Module. • Diseases, • Insects, • Weeds, • Nematodes, • Physiological Disorders. • Cost Benefit Analysis among Varities. • Ability for Domain Experts to define problems online and create decision trees to solve the problems. • Powerful Administrative Module. • Full Featured Online Help.

16. Add New Crops

17. Knowledge Model for Crops

18. Economic Attributes

24. Crop Specific Disease Information

26. Add New Insect

27. Add New Insect

28. Insect Image Updating

29. New Initiatives • Ontology based Expert System

30. What is Ontology? • Controlled vocabulary that describes objects and the relations between them. • Has grammar for using the vocabulary terms to express something meaningful. • Together with set of individual instances of classes constitutes a Knowledgebase. • Classes describe concepts in the domain.

31. Ontology Based Expert System • In Rule-based systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledgebase (Rules and Facts). • In an Ontology Based Expert System domain knowledge is stored in ontology. • Ontology allows better way of representation of knowledge and tools are available for its easy creation. • Ontology is a part of Semantic Web Technologies and its use can help in building more scalable and multi agent based systems.

32. Advantages of OBES • It is easy to maintain as only the central server needs to be maintained. • All the data and user transactions are captured in a single central database. • It can be quickly deployed. • It works irrespective of the operating system of the user. • It can be used by the user or Web Service client or software agent. • Domain experts can dynamically update their knowledge in ontology.

33. TECNOLOGY REVIEW

34. XML • Allows users to define their own elements. • Primary purpose to help information systems share structured data. • Clear, simple syntax and unambiguous structure . • Offers many ways to check the quality of document. • Basic syntax for one element is: • <el_name attrname=“attr_value”>el_content</el_name>

35. RDF • Encoding knowledge for the semantic web. • Builds on existing XML and URI technologies. • URIs used to identify resources and make statements about them. • Statement consist of RDF triples.

36. [resource] [property] [value] Crop  affectedBy Disease [subject] [predicate] [object] RDF Triple Crop affectedBy Disease

37. RDF Tags • Defined triples can be encoded in RDF/XML. • RDF/XML syntax: • rdf:Description - define a Triple. • rdf:about – define subject of triple. • Properties are defined by their URI as tag using xml namespace. • Value of property tag can be plain/typed literal or a resource. • rdf:resource – defines value of a property if it is a resource. • rdf:datatype – defines data type of literals . • Only for describing resources not for specifying the semantics

38. RDFS • Describe groups of related RDF resources and the relationships between them • Defines allowable properties that can be assigned to RDF resources • Allows creating classes of resources that share common properties • Resources defined as instances of classes • Class is a resource • Any class can be a subclass of another 38

39. RDFS • RDFS tags: • rdfs:Class : define a class in RDFS. • rdfs:subClassOf : assign a class its parent class. • rdf:Property :define a property . • rdfs:subPropertyOf : assign a property its parent property. • rdfs:domain and rdfs:range : schema properties to describe application specific properties. • rdfs:Resource : RDFS defines all the classes as subclass of this class 39

40. Web Ontology Language (OWL) • Builds upon RDF and RDFS • Uses XML to indicate hierarchies and relationships between different resources • Has three sub languages: OWL Lite, OWL DL, and OWL Full

41. Need for OWL over RDFS • Classes can be defined as Boolean combinations of other classes . • It can be stated that the two classes (with different URI) are same. • Cardinality restrictions can be specified for properties. • It can be specified that a property is transitive, symmetric, Functional, inverseOf, or InverseFunctional Property.

42. Java Server Pages (JSP) • Enables rapid development of platform independent Web-based applications. • Separates the user interface from the underlying dynamic content. • Uses XML-like tags that encapsulate the logic that generates the content for the page. • JSPs are compiled into JavaServlets .

43. JENA • Java framework for building Semantic Web applications. • It provides a programmatic environment for RDF, RDFS and OWL, including a rule-based inference engine. • An ontology model is an extension of the Jena RDF model.

44. JENA Interface • Model: a set of statements. • Statement: a triple of {R,P,O}. • Resource: subject, URI. • Property: “characterstic” of resource. • Object: may be a resource or a literal. • Literal: non-nested “object”. • Container: special resource, collection of things.

45. Protégé • Free, open-source platform which provides tools to construct domain models and knowledge-based applications . • Supports the creation, visualization, and manipulation of ontologies . • Protégé-OWL is tightly integrated with Jena.

46. Protégé • Protégé platform supports two main ways of modeling ontologies. • The Protégé-Frames editor enables users to build and populate ontologies that are. frame-based. • The Protégé–OWL editor enables users to build ontologies for the Semantic Web in particular in the W3C's Web Ontology Language (OWL).

47. Protégé The Protégé-OWL editor enables users to: • Load and save OWL and RDF ontologies. • Edit and visualize classes, properties, and restrictions . • Define logical class characteristics as OWL expressions. • Edit OWL individuals.

48. Creating a subclass ( Cereals ) of owl:Thing.

49. Adding restrictions for class diseases

50. All the Object properties defined for crop ontology