Traffic Crash Report Information Search System

1. Traffic Crash Report Information Search System Kiavash Bahreini – 055251

2. 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

3. 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

4. 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.

5. 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.

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

7. 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.

8. 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

9. 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

10. Traffic crash Ontology in Protégé

12. 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.

13. 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.

14. 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.

15. Java Server Pages • JavaServer Pages (JSP) technology allows web developers and designers to • rapidly develop • Easily maintain information-rich, • Dynamic web pages

16. 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 JavaServerPages(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

17. Traffic crash system use case, implementation and execution

18. JBuilder 2006 Computation Engine Jena 2.2 OWL API Inference Engine User interface JSP Pages RDQL Serarch Query Protege 3.2 OWL File Ontology & Knowledge Base Traffic crash system use case, implementation and execution • Execution of program in Browser

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

20. Traffic crash system use case, implementation and execution • Running queries in Browser • 1)Lists of whole injurers:

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

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

23. Traffic crash system use case, applying annotation: • Running queries in Browser • 1) Smith information:

24. Traffic crash system use case, implementation and execution • Annotation output:

25. Traffic crash system use case, implementation and execution • Annotation output (continue):

26. Traffic crash system use case, implementation and execution • Running queries in Browser • 2)Select injurers with conditions:

27. Traffic crash system use case, implementation and execution • Output:

28. Traffic crash: Reasonable Search engine • Running queries in Browser • 3)The minimum crash in which season was occured ? :

29. Traffic crash: Reasonable Search engine • Output:

30. Traffic crash: Reasonable Search engine • Continue output:

31. Traffic crash: Insert into Owl file • output:

32. Traffic crash: Delete from Owl file • output:

33. Traffic crash system use case, implementation and execution • Some source codes for classes in traffic crash ontology:

34. 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’.

35. 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.

36. References • [1]- T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web. Scientific American, 284(5), 34–43, 2001. • [2]- O. Lassila, R.R. Swick, Resource Description Framework (RDF) Model and Syntax Specification, http://www.w3.org/TR/REC-rdf-syntax/. • [3]- D. Brickley, R.V. Guha, RDF Vocabulary Description Language 1.0: RDF Schema, http://www.w3.org/TR/rdfschema/. • [4]- M. Kifer, G. Lausen, J. Wu, Logical foundations of object-oriented and frame-based languages. Journal of the ACM, 42, 741–843, 1995. • [5]- D. Fensel, I. Horrocks, F. van Harmelen, S. Decker, M. Erdmann, M. Klein, OIL in a nutshell. In Knowledge Acquisition, Modeling, and Management, Proceedings of the European Knowledge Acquisition Conference (EKAW-2000), October, pp. 1–16. Springer-Verlag, Berlin, 2000. • [6]- P.F. Patel-Schneider, P. Hayes, I. Horrocks, F. van Harmelen, Web Ontology Language (OWL) Abstract Syntax and Semantics, http://www.w3.org/TR/owl-semantics/, November 2002. • [7]- C. Davis, S. Jajodia, P. Ng, R. Yeh (eds), Entity-Relationship Approach to Software Engineering: Proceedings of the 3rd International Conference on Entity-Relationship Approach, Anahein, CA, 5–7, October. North- Holland, Amsterdam, 1983. • [8]- Tim Berners-Lee, James Hendler, and Ora Lassila. The semantic web. Scientific American, 2001(5), 2001. • [9]- Semantic Web Technologies Trends and Research in Ontology-based Systems John Davies BT, UK Rudi Studer University of Karlsruhe, Germany Paul Warren BT, UK John Wiley & Sons Ltd. • [10]- http://www.w3.org/2004/OWL/ • [11]- http://www.w3.org/RDF/ • [12]- The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management Michael C. Daconta Leo J. Obrst Kevin T. Smith • [13]- I. Horrocks, “DAML+OIL: A Description Logic for the Semantic Web.” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, IEEE, Vol. 25, No. 1, pp. 4-9, 2002.

37. 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 • [18]- http://www.Borland.com/ • [19]- Jena – A Semantic Web Framework for Java, URL: • http://jena.sourceforge.net/. • [20]- HP Labs Semantic Web Research, URL: • http://www.hpl.hp.com/semweb/. • [21]- http://jena.sourceforge.net/tutorial/RDQL/ • [22]- Borland JBuilder 2006 Documentation Files. • [23]- http://www.hewettresearch.com/mikehewett.html • [24]- http://jatha.sourceforge.net/ • [25]- http://jakarta.apache.org/site/binindex.html

38. Thank you • Any Questions?