Design of Ontology Based Ubiquitous Web for Agriculture - A farmer helping system

Shyamaladevi.K1 T.T. Mirnalinee2 Tina Esther Trueman3 Kaladevi.R4

1,3,4Department of Computer Science & Engineering, Anna University of Technology, Chennai. 2Department of Computer Science and Engineering, SSN College of Engineering, Chennai.

Abstract- Agriculture census information is a leading source of a country’s development. Such information is used by many who provide services to farmers and rural communities. The human interaction system provides a move from entity and object centric processing to relationship and event centric processing. The computer is interacted efficiently with the human for giving the solution to their problems. The integrated system gives the ability to extract, represent and reason about a variety of relationship as well as providing integral support. The proposed system is called farmer helping system which integrates relevant web services like soil information, plant disease information, and plant information and also contains pesticides and fungicides information. This farmer helping system gives the appropriate solution for farmers. The farmer helping system analyses the message from the user, contacts appropriate resources, and return actionable information, while requiring minimal involvement or technology consciousness from the user. The semantically annotated data is used for integration, search, analysis, discovery, question answering and situational awareness for making the user system efficient. This system can help for agricultural development planning and formulate of agricultural policies. Keywords: semantics, ontology, Interoperability, Farmer helping system

I. INTRODUCTION

Human-based computation is a computer

science technique in which a Computational process performs

its function by outsourcing certain steps to humans [1]. This

approach uses differences in abilities and alternative costs

between humans and computer agents to achieve symbiotic

human-computer interaction. Human-based computation

methods combine computers and humans in different roles.

Human–computer Interaction (HCI) involves the study,

planning, and design of the interaction between people (users)

and computers. Interaction between users and computers

occurs at the user interface (or simply interface), which

includes both software and hardware. The user interface, in

the industrial design field of human–machine interaction, is

the space where interaction between humans and machines

occurs. The goal of interaction between a human and a

machine at the user interface is effective operation and control

of the machine, and feedback from the machine which aids the

operator in making operational decisions.

The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily [2]. Humans are capable of using the Web to carry out tasks such as finding the Irish word for "folder", reserving a library book, and searching for the lowest price for a DVD. However, machines cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web. The Semantic Web, as originally envisioned, is a system that enables machines to "understand" and respond to complex human requests based on their meaning. Such an "understanding" requires that the relevant information sources is semantically structured, a challenging task. Web Services [3] are loosely specified and coupled

components distributed over the internet with the purpose of

being accessed and used ubiquitously by suppliers, customers,

business and trading partners. The basic service oriented

architecture is based on the publishing of a service by a

service provider, the location of a service by a service

requestor and the interaction between the two based on the

service description. A multitude of proposed standards and pro

ducts have emerged in an attempt to meet the needs of this

world wide community of web services adopters. The core

established standards include the Web Services Description

Language (WSDL) [4], the Simple Object Access Protocol

(SOAP) [5] and the Universal Description, Discovery and

Integration (UDDI) [6]. The Web services Inspection

Language (WSIL) [7] is a more light weight yet

complimentary specification for service discovery

A Service-Oriented Architecture (SOA) is a set of

principles and methodologies for designing and developing

software in the form of interoperable services [8]. These

services are well-defined business functionalities that are built

as software components that can be reused for different

purposes. SOA design principles are used during the phases

of systems development and integration.SOA also generally

provides a way for consumers of services, such as web-based

applications, to be aware of available SOA-based services.

The heterogeneity of sensor data is minimized by

ontology mapping. Ontology mapping is the process of finding

semantic correspondence between similar elements belonging

to different ontology. The services like each weather

forecasting systems and its interoperability and the ontological

representation are available through Service Oriented

Architecture’s registry Universal Description, Discovery and

Integration (UDDI) [6]. A knowledgebase is a collection of

Ontology Web Language (OWL) [9] statement about

resources. Then querying language is used to retrieve

information from the knowledgebase.

This paper is organized as follows; Section II

provides brief information about related works. Section III

explains the proposed system architecture and its components

in detail. Section IV concludes the proposed work and gives

directions for future use.

II. RELATED WORK

Amit Sheth et al [10] proposed Semantic Sensor web

(SSW) where sensor data is annotated with semantic metadata

to increase interoperability and provide contextual information

for situational knowledge. A semantic sensor data would

contain spatial, temporal, and thematic information essential

for discovering and analyzing sensor data. The SSW

framework is tested over weather data using complex queries.

Cory A. Henson et al [11] proposed Semantic Sensor

Observation Service where Integration of Semantics, sensor

and services to increase the interoperability between

heterogeneous sensor data and application that use the data.

Benefits of integrating the sensor web with the semantic web.

OGC and the semantic web language define and provide a

platform for integration and reasoning over sensor observation

in order to attain shared knowledge of an environment.

Surya S. Durbha et al [12] described architecture

about the costal sensor web in Based Middleware and Tools

for Coastal Sensor Web Applications where OGC sensor web

enablement framework standard is used to develop syntactic

data models and semantic enrichment is through ontology. A

coastal semantic mapping (COSEM-MAP) tool developed as a

part of this work facilitates the harmonization of different

representation. Semantic heterogeneities are resolved.

Semantic heterogeneities are resolved. A hybrid algorithm

presented to discover mappings between different applications

enhancement of the UDDI registry by combing it with OWL-S

to perform semantic search of web services.

Krzysztof Janowicz et al [13] described about Five

challenges for the Semantic Sensor Web where the challenges

is related to the abstraction level in which sensor data can be

obtained, processed and managed, the adequate

characterization and management of the quality(QOS) of

sensor data. Integration and fusion of data coming from sensor

networks. Identification and location of relevant sensor-based

data sources. Rapid development of applications that is able to

handle sensor data. These challenges are being addressed

using semantic based approaches.

Ruoyan Zhang et al [14] proposed Automatic

Composition of Semantic Web Services where the automatic

composition a user is not involved the system defines control

and data flow by assembling the individual services. Selecting

the best possible service to avoid the complexity and improve

efficiency. Using Interface matching automatic service

composition technique is to find a composition that produces

the desired output. Composition cannot proceed automatically

and ambiguities in matching services.

Yannis Kalfoglou et al [15] designed the semantic

web system in this paper, On the Emergent Semantic Web and

Overlooked Issues where to deliver knowledge sharing in an

environment such as the Semantic Web in effective and

efficient manners. Issues, associated with agents and trust to

hidden assumptions made with respect to knowledge

representation and robust reasoning in a distributed

environment. These issues could potentially hinder further

development if not considered at the early stages of designing

Semantic Web systems.

Bernd Resch et al [16] described about the

Geographic awareness through this paper, Enabling

Geographic Situational Awareness in Emergency Management

Research where a prototype application named eMapBoard,

which implements the geo-collaboration concept and

demonstrates the benefits of web-based geo information

systems by offering a range of simple GIS tools. Its

functionality and usability was evaluated during GNEX06, a

near real-time exercise simulating an accident in a nuclear

power plant. Concluding, it can be stated that eMapBoard has

proven an easy-to-use geo-collaboration tool, which simplifies

the cooperation between different involved parties such as

local authorities, the mission control centre, action forces and

other decision makers.

Amit Sheth [17] described architecture for

Computing for Human Experience Semantics-Empowered

Sensors, Services, and Social Computing on the Ubiquitous

Web where Analyzes any form of Web, social, or sensor data

by extracting metadata, resulting in comprehensive semantic

annotation. This process is aided by conceptual models and

knowledge and by a variety of information-retrieval,

statistical, and AI (machine learning and natural-language

processing) techniques, at the Web scale. Semantic analysis

supported by mining, inference, and reasoning over

annotations supports applications.

III. PROPOSED WORK

We designed the farmer helping system using the

semantic web. Semantic annotation is achieved through the

domain ontology. Figure 1 shows the architectural diagram of

our proposed system.

Fig. 1.Semantic web architecture for the proposed system.

Using semantic web, the farmer helping system is

designed. The farmer sends the query to the system regarding

the unknown crop disease. The system analyzes all the

resources in the system and gives the appropriate solution to

the farmer. The farmer helping system requires soil

information system, weather information system, plant

information system, pesticides and fungicides information

system. To integrate all the system the farmer helping system

is designed and interacts with human to give actionable

information.

The sensor data is getting from the sensor

observation system then the data is annotated with the

meaningful information. Semantic annotation is achieved

through domain ontology. Then the data is interpreted in to

machine readable format. This semantically annotated data is

as an input data to our system.

�A. Farmer Helper System

Using the semantic web the farmer helping system is

designed. The farmer helper system requires weather

information system, soil information system, plant information

system, pesticides and fungicides information system.

Integrate all the services by getting all the information from

the respective information systems. The farmers send a

message requesting help with an unknown crop disease. The

system analyzes the message, contacts appropriate sources,

and returns actionable information, with requiring minimal

involvement or technology consciousness from the farmer.

The farmer helping system would also have a feedback

mechanism, prompting the farmer for progress and informing

the community when metrics deviate from known

specifications. The system would do all these things while

requiring minimal involvement from the farmer. Figure 2

shows the Farmer helping system.

Fig. 2. Architecture of Farmer helping system.

B. Sensor data The first layer is the data source layer which consists

of heterogeneous Sensor data from various sensor observation

systems. The weather data is extracted from the weather

sensor observation system. Likewise the soil information is get

from the soil sensor observation system.

C. Ontology description Once the sensor data is available we need to describe

the semantics of this data. Therefore, the second layer of our

architecture consists of sensor ontology. The weather and soil

data’s ontology are developed and to form the farmer’s system

ontology. Ontology [18] is an approach of knowledge

representation. Ontology is a specification of a

conceptualization. Ontology is an explicit description of a

domain which is concepts, properties and attributes of

concepts, constraints on properties and attributes and

Individuals. Ontology defines a common vocabulary and a

shared understanding.

Fig. 3. Domain ontology for weather system.

Figure 3 shows the Domain Ontology for Weather

system. The weather data is the super class of the ontology.

The subclass are temperature, moisture, humidity, wind speed,

atmosphere. The attribute of the temperature is

min_Temperature, max_Temperature. The attribute of the

wind speed is max_Wind_Speed, min_Wind_Speed. It also

describe the Has a relationship and Is a relationship. Weather

has temperature, moisture, humidity, wind speed and

atmosphere .The same way that temperature has minimum and

maximum temperature. Every attribute considered as subclass

is associated with the annotation described in the fig. 3 for

humidity. It denotes the annotation which is that the

description of the attribute. Likewise every data are annotated.

Fig. 4.Domain ontology for soil system.

� Figure 4 shows the Domain Ontology for Soil

system. The soil class contains red soil, black soil, yellow and

red soil, alluvial soil, mountain soil, desert soil, saline soil.

The soil class is the super class. Rest of the all is sub class

information. These are all comes under the Has relationship.

Every attribute considered as subclass is associated with the

annotation described in the fig. 4 for Forestsoil. It denotes the

annotation which is that the description of the attribute.

D. Semantic Web The Semantic Web, as described by the W3C

Semantic Web Activity, is an evolving extension of the World

Wide Web in which the semantics, or meaning, of information

on the Web is formally defined. Formal definitions are

captured in ontologies, making it possible for machines to

interpret and relate data content more effectively. The

principal technologies of the Semantic Web include the

Resource Description Framework (RDF) data representation

model, and the ontology representation languages RDF

Schema (RDF-S) and Web Ontology Language (OWL). In

addition to these representation languages, an RDF query

language called SPARQL (SPARQL Protocol and Rdf Query

Language) [19] is now a W3C recommendation and the

common method of querying ontological data.

E. Query Engine Once we defined the ontology for sensor data mapping, we need to define a method for querying the data. SPARQL [19] is the candidate recommendation of W3C for querying RDF/OWL [9] data graphs and designed specifically to support semantic web applications. Once the raw sensor

data is transformed into RDF/OWL format we can use SPARQL to run queries. For instance, Table 1 shows the SPARQL code to define the weather data.

TABLE I. WEATHER SPARQL QUERY

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX owl: <http://www.w3.org/2002/07/owl#> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT? Moisture? Weather WHERE { ?moisture rdfs:subClassOf ?weather}

<http:meterology:p1> <http:meterology:p1> <http:meterology:p1>�

Humidity Moisture Wind speed�

77 89 74�

The SPARQL query results are returned as an XML document in a format known as “SPARQL Variable Binding Results XML Format”.

TABLE II. SOIL SPARQL QUERY

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX owl: <http://www.w3.org/2002/07/owl#> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT? Moisture? Weather WHERE { ?moisture rdfs:subClassOf ?weather}

<http:soil info:p1> <http:soil info:p1> <http:soil info:p1>

Red soil Black soil Desert soil

F. Rule Based Engine The rules are created for which weather and which soil is best for the plantation of crop and also used to describe the pesticides and fungicides of the particular disease.

TABLE III. RULE FOR PESTICIDES AND DISEASES

Example rule file

@prefixns:http://www.sensorontology.comr.owl#@in

clude<OWL>

[Crop Disease:(?m rdf ns:yellow leaf in sweet corn)

(?Use NPK)]

[Crop Disease:(?m rdf ns:Tomato leaves wilt)

(?Use Ammonium chloride)]

<? Xml version="1.0"?> <! DOCTYPE Ontology [ <! ENTITY xsd "http://www.w3.org/2001/XMLSchema#" > <! ENTITY xml "http://www.w3.org/XML/1998/namespace" > <! ENTITY rdfs "http://www.w3.org/2000/01/rdf-schema#" > <! ENTITY RDF "http://www.w3.org/1999/02/22-rdf-syntax-ns#"]> <Ontology xmlns="http://www.w3.org/2002/07/owl#" xml:base="http://www.semanticweb.org/ontologies/2012/0/Ontology1325491331955.owl" Xmlns: rdfs="http://www.w3.org/2000/01/rdf-schema#" Xmlns: xsd="http://www.w3.org/2001/XMLSchema#" Xmlns: rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xml="http://www.w3.org/XML/1998/namespace" ontologyIRI="http://www.semanticweb.org/ontologies/2012/0/Ontology1325491331955.owl"> <Prefix name="xsd" IRI="http://www.w3.org/2001/XMLSchema#"/> <Prefix name="owl" IRI="http://www.w3.org/2002/07/owl#"/> <Prefix name="" IRI="http://www.w3.org/2002/07/owl#"/> <Prefix name="rdf" IRI="http://www.w3.org/1999/02/22-rdf-syntax-ns#"/> <Prefix name="rdfs" IRI="http://www.w3.org/2000/01/rdf-schema#"/> <Declaration> <Class IRI="#Atmosphere"/> </Declaration> <Declaration> <Class IRI="#Humidity"/> </Declaration><Declaration> <ObjectExactCardinality cardinality="1"> <Object Property abbreviatedIRI=": topObjectProperty"/> <Class IRI="#Max_temperature"/> </ObjectExactCardinality> </EquivalentClasses> <EquivalentClasses> </Declaration><Declaration> <Declaration> <Class IRI="#Humidity"/> </Declaration><Declaration> <ObjectExactCardinality cardinality="1"> <Object Property abbreviatedIRI=": topObjectProperty"/> <Class IRI="#Max_temperature"/>

The system finds the diseases of particular

crop then the rule based method methods are used to find the

pesticide and fungicide information. For example if the

disease is yellow leaf in the sweet corn crop, use NPK

pesticides. If it is leaves wilt in Tomato plant, use ammonium

chloride.

G. UDDI

Universal Description, Discovery and Integration [6]

is a platform independent, Extensible Markup

Language (XML)-based registry for businesses worldwide to

list themselves on the Internet and a mechanism to register and

locate web service applications. UDDI is an open industry

initiative, sponsored by the Organization for the Advancement

of Structured Information Standards (OASIS), enabling

businesses to publish service listings and discover each other

and define how the services or software applications interact

over the Internet. UDDI was originally proposed as a

core Web service standard. It is designed to be interrogated

by SOAP messages and to provide access to Web Services

Description Language (WSDL) documents describing the

protocol bindings and message formats required to interact

with the web services listed in its directory.

H. SOAP

SOAP [5] is a lightweight protocol for exchange of

information in a decentralized, distributed environment. It is

an XML based protocol that consists of three parts: an

envelope that defines a framework for describing what is in a

message and how to process it, a set of encoding rules for

expressing instances of application-defined data types, and a

convention for representing remote procedure calls and

responses. SOAP can potentially be used in combination with

a variety of other protocols; however, the only bindings

defined in this document describe how to use SOAP in

combination with HTTP and HTTP Extension Framework.

I. WSDL

The Web Services Description Language (WSDL)

[4] is an XML-based language that is used for describing the

functionality offered by a Web service. A WSDL description

of a web service provides a machine-readable description of

how the service can be called, what parameters it expects and

what data structures it returns. It thus serves a roughly similar

purpose as a Method signature in a programming language.

�

IV. CONCLUSION� The farmer helping system is designed using the

semantic web which allows data to be shared and reused

across application enterprise and communities. Continuous

semantics is supported by knowledge that’s dynamic and

updated through automated techniques and user interaction

with the knowledge. This is the system which provides

semantic and integrated based approach to disseminate

essential information to the user. This system can help for

agricultural development planning and formulate of

agricultural policies.. We designed a framework by integrating

agriculture information using semantic web which provides

services to farmers and rural communities. This system could

be further enhanced by associating certain rules and

environment learning technologies to adapt the dynamic

changes.

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