traffic crash report information search system

Traffic Crash Report Information Search System

Kiavash Bahreini – 055251

Outline

Introduction Semantic Web Search on the Web Adaptable Inference Capabilities The Traffic crash Ontology(properties)

Protégé Traffic crash Ontology in Protégé JBuilder 2006 Jena 2.2 Ontology API RDQL Java Server Pages Tomcat Traffic crash system use case, implementation and execution

Outline (cont)

Execution of program in Browser by running queries Converting Owl file to rdf file Compare RDBMS with OWL queries Applying annotation Reasonable Search engine Insert into Owl file Delete from Owl file Some source codes for classes in traffic crash ontology Comparison of Semantic Search and Regular Search Conclusion References

Introduction

The traffic crash database provides information on the frequency and severity of crashes within the state, demographic characteristics of individuals involved in crashes as well as weather, lighting or other related conditions associated with the crash incident.

Semantic Web

Common framework Allows data

Sharing Reuse

Across domains Application Enterprise Community boundaries

Based on Resource Description Framework (RDF) XML for syntax URIs for naming.

Search on the Web

Seeking information on the Web is widely used and will become more important as the Web grows. Nowadays, search engines browse through the Web seeking given terms within web pages or text documents without using ontologies.

Traditional search engines such as Yahoo are based on full-text search. These search engines are seeking documents, which contain certain terms.

Adaptable Inference Capabilities

Inference mechanisms for deduction of information not explicitly asserted is an important characteristic of ontology-based systems. However, systems with very general inference capabilities often do not take into account other needs, such as scalability and concurrency.

The Traffic crash Ontology(properties)

The content of Traffic crash report is: Age Gender Driver/Occupant Non-Occupant Type Pedestrian Visibility Safety Equipment Seat Position Type of Vehicle Collision Type Injury Severity Severity of Crash Weather Conditions Lighting Conditions Time Period Day of Week County of Occurrence

Protégé

Protégé is an ontology editor knowledge-base editor an open-source, Java tool

provides extensible architecture to create customized knowledge-based applications.

Developed by Stanford University, USA

Traffic crash Ontology in Protégé

JBuilder 2006

The system is written in JBuilder 2006. JBuilder is and IDE (Integrated

Development Tools) for developing new application, web etc software based on Java

Language. All of the packages and classes for using OWL and running queries are

imported into this IDE.

Jena 2.2 Ontology API

Jena 2.2 Ontology API is a Java framework for building Semantic Web applications. Use

RDF models in your Java applications with the Jena Semantic Web Framework.

RDQL

RDQL is a query language for RDF in Jena models. The idea is

to provide a data-oriented query model so that there is a more

declarative approach to complement the fine-grained,

procedural Jena API.

Java Server Pages

JavaServer Pages (JSP) technology allows web developers and

designers to rapidly develop

Easily maintain information-rich, Dynamic web pages

Tomcat

Tomcat is the official reference implementation of the Java Servlet 2.2 and JavaServer Pages 1.1 technologies.

Tomcat is a servlet container and JavaServer Pages(tm) implementation.

It may be used stand alone, or in conjunction with several popular web servers: Apache, version 1.3 or later Microsoft Internet Information Server, version 4.0 or later Microsoft Personal Web Server, version 4.0 or later Netscape Enterprise Server, version 3.0 or later

Traffic crash system use case, implementation and execution

Traffic crash system use case, implementation and execution Execution of program in Browser

JBuilder 2006

Protege 3.2

OWL File

Ontology & Knowledge Base

User interface

JSPPages

Computation Engine

Jena 2.2 OWL API

Inference Engine

RDQLSerarch Query

Traffic crash system use case, implementation and execution http://app.idph.state.il.us/emsrpt/crash.asp

Traffic crash system use case, implementation and execution

Running queries in Browser 1) Lists of whole injurers:

String queryString = " SELECT ?subjectName" + " WHERE ( ?in, nss:" + predicateName + ", ?

objectName)" + " USING nss FOR <" + NS + ">";

Traffic crash system use case, Converting Owl file to rdf file(output)

Traffic crash system use case, Converting Owl file to rdf file(generated rdf)

<?xml version="1.0" encoding="windows-1252"?><rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:vcard="http://www.w3.org/2001/vcard-rdf/3.0#">

<rdf:Description rdf:about="http://www.owl-ontologies.com/TrafficCrash.owl#">

<vcard:LABEL rdf:parseType="Resource"> <vcard:Given>hadSafetyEquipmentUsed</vcard:Given>

<vcard:Suffix>"Helmet"</vcard:Suffix> <vcard:Prefix>Milad</vcard:Prefix>

</vcard:LABEL> <vcard:LABEL rdf:parseType="Resource">

<vcard:Suffix>"Other"</vcard:Suffix> <vcard:Prefix>Samuel</vcard:Prefix>

<vcard:Given>hadSeatPosition</vcard:Given> </vcard:LABEL> </rdf:Description>

</rdf:RDF>

Traffic crash system use case, applying annotation:

Running queries in Browser 1) Smith information:

<owl:AnnotationProperty rdf:ID="SmithAnnotation"> <rdf:type

rdf:resource="http://www.w3.org/2002/07/owl#DatatypeProperty"/>

<rdfs:range> <owl:DataRange>

<owl:oneOf rdf:parseType="Resource"> <rdf:first

rdf:datatype="http://www.w3.org/2001/XMLSchema#string"

>Smith</rdf:first> <rdf:rest rdf:resource="http://www.w3.org/1999/02/22-

rdf-syntax-ns#nil"/> </owl:oneOf>

</owl:DataRange> </rdfs:range>

<rdfs:domain rdf:resource="#Injured"/> </owl:AnnotationProperty>

Traffic crash system use case, implementation and execution

Annotation output:

Traffic crash system use case, implementation and execution

Annotation output (continue):

Traffic crash system use case, implementation and execution

Running queries in Browser 2) Select injurers with conditions:

String queryString = " SELECT ?GenderType ?Age ?SeatPosition ?PedestrianVisibility ?LightingConditions ?TypeOfVehicle ?SeverityOfCrash ?NonOccupantType ?

PassengerType ?DayOfWeek ?SafetyEquipmentUsed ?CountyOfOccurrence ?InjurySeverityNonMotorVehicle ?Weather ?TimePeriod ?CollisionType ?

InjurySeverityMotorVehicle" + " WHERE ( nss:" + InjuredName + ", nss:hasGenderType , ?GenderType)" +

" ( nss:" + InjuredName + ", nss:hasAge, ?Age)" + " ( nss:" + InjuredName + ", nss:hadSeatPosition , ?SeatPosition)" +

" ( nss:" + InjuredName + ", nss:hadPedestrianVisibility , ?PedestrianVisibility)" + " ( nss:" + InjuredName + ", nss:hadLightingConditions , ?LightingConditions)" +

" ( nss:" + InjuredName + ", nss:hadTypeOfVehicle , ?TypeOfVehicle)" + " ( nss:" + InjuredName + ", nss:hadSeverityOfCrash , ?SeverityOfCrash)" +

" ( nss:" + InjuredName + ", nss:hadNonOccupantType , ?NonOccupantType)" + " ( nss:" + InjuredName + ", nss:hadPassengerType , ?PassengerType)" +

" ( nss:" + InjuredName + ", nss:hadDayOfWeek , ?DayOfWeek)" + " ( nss:" + InjuredName + ", nss:hadSafetyEquipmentUsed , ?SafetyEquipmentUsed)"

+ " ( nss:" + InjuredName + ", nss:hadCountyOfOccurrence , ?CountyOfOccurrence)" +

" ( nss:" + InjuredName + ", nss:hadInjurySeverityNonMotorVehicle , ?InjurySeverityNonMotorVehicle)" +

" ( nss:" + InjuredName + ", nss:hadWeather , ?Weather)" + " ( nss:" + InjuredName + ", nss:hadTimePeriod , ?TimePeriod)" +

" ( nss:" + InjuredName + ", nss:hadCollisionType , ?CollisionType)" + " ( nss:" + InjuredName + ", nss:hadInjurySeverityMotorVehicle , ?

InjurySeverityMotorVehicle)" + " USING nss FOR <" + NS + ">";

Traffic crash system use case, implementation and execution

Output:

Traffic crash: Reasonable Search engine

Running queries in Browser 3) The minimum crash in which season was occured ? :

for (int k = 0; k < tokenArray.length; k++) { if (tokenArray[k].equalsIgnoreCase("season") && tokenArray[k]!=null) {

k=tokenArray.length; for (int j = 0; j < tokenArray.length; j++) {

if (tokenArray[j].equalsIgnoreCase("crashes") || tokenArray[j].equalsIgnoreCase("crash") || tokenArray[j].equalsIgnoreCase("accident") && tokenArray[j]!=null) {

j=tokenArray.length; for (int i = 0; i < tokenArray.length; i++) {

if (tokenArray[i].equalsIgnoreCase("min") || tokenArray[i].equalsIgnoreCase("minimum") || tokenArray[i].equalsIgnoreCase("atleast") || tokenArray[i].equalsIgnoreCase("max") || tokenArray[i].equalsIgnoreCase("maximum") || tokenArray[i].equalsIgnoreCase("atmost") &&

tokenArray[i]!=null) { i=tokenArray.length;

for (int c = 0; c < readFile.length(); c++) { int startIndex = readFile.indexOf("<hadWeather rdf:datatype=\"http://www.w3.org/2001/XMLSchema#string\"", c);

if (startIndex != -1) { int startIndex1 = readFile.indexOf(">", startIndex+66);

int nameIndex = readFile.indexOf("</hadWeather>", startIndex1 + 1); reasonArray[reasonCounter++] = new String(readFile.substring(startIndex1 + 1, nameIndex));

c = nameIndex + 13; weather=true;

} }

} }

} }

} }

}

Traffic crash: Reasonable Search engine

Output:

Traffic crash: Reasonable Search engine

Continue output:

Traffic crash: Insert into Owl file

output:

Traffic crash: Delete from Owl file

output:

Traffic crash system use case, implementation and execution

Some source codes for classes in traffic crash ontology:

<rdfs:domain rdf:resource="#Injured"/> <rdfs:range> <owl:DataRange> <owl:oneOf rdf:parseType="Resource"> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Back</rdf:first> <rdf:rest rdf:parseType="Resource"> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Front</rdf:first> <rdf:rest rdf:parseType="Resource"> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Other</rdf:first> <rdf:rest rdf:parseType="Resource"> <rdf:rest rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#nil"/> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Unknown</rdf:first> </rdf:rest> </rdf:rest> </rdf:rest> </owl:oneOf> </owl:DataRange> </rdfs:range> </owl:DatatypeProperty>

Comparison of Semantic Search and Regular Search In RDBMS model, there is no reasoner-based

system to infer information but here there exist.

We cannot use inference engines in RDBMS. We cannot use unsupervised learning in

RDBMS. We cannot use supervised learning in

RDBMS In semantic approach the data being marked

provides the possibility for computers and other digital agents to process and ‘understand’.

Conclusion

The Traffic Crash Report Form is used to report traffic crashes.

It is based on reasonable-based system. Platform independence. Tools independence. User friendly based on web pages. It allows usingmany annotating techniques. It allows users to search, add, and delete

information from web pages.

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References (Cont1)

[14]- Protégé overview, URL: http://protege.stanford.edu, last visited: June 2006 15]- N. F. Noy, M. Sintek, S. Decker, M. Crubezy, R. W. Fergerson, & M. A. Musen.

“Creating Semantic Web Contents with Protege-2000”, IEEE Intelligent Systems 16(2):60-71, 2001

[16]- J. Gennari, M. A. Musen, R. W. Fergerson, W. E. Grosso, M. Crubézy, H. Eriksson, N. F. Noy, S. W. Tu, “The Evolution of Protégé: An Environment for Knowledge-Based Systems Development”, 2002, URL: http://smi-web.stanford.edu/pubs/SMI_Reports/SMI-2002-0943.pdf

[17]- Erhan Gayde Thesis Eastern Mediterranean University September 2006, Gazimağusa, North Cyprus

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Thank you

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