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  1. 1. Developing Ontology-basedSemantic Web Applicationfor Biological DomainAuthor : Kashif Iqbal
  2. 2. Semantic Web for biological sciences 2Agenda Introduction Problem Statement Motivation Literature Review Methodology & Procedures Implementation & Benefits
  3. 3. Introduction The current Web represents informationusing natural language, graphics andmultimedia (Ivan Herman). Humans can process this information easily They can deduce facts from partial information They can create mental associations. They use to various sensory information.Semantic Web for biological sciences 3
  4. 4. Introduction Tasks often require to combine data on theWeb: Plant flora and Gene sequencing informationmay come from different sites. searches in different digital libraries etc. Again, humans combine these information atedious process. even different terminologys are used!Semantic Web for biological sciences 4
  5. 5. Introduction However: machines are ignorant To make machines intelligent ontologys lie atthe foundation which provide sophisticatedframeworks to model the knowledge of adomain. The Semantic Web provides technologies tomake it possible! For example: RDF ,OWL,SPARQL,OWL-API, User-Interface.Semantic Web for biological sciences 5
  6. 6. Semantic Web for biological sciences 6 In the Life Science domain, a number ofdocuments presents already large on the weband continues to grow at an exponential rate. Current search engines not support forretrieving the information; millions of web documents retrieved also most of the data is not publicallyaccessible due to the concept terminology. The problem is how to provide the betterinformation retrieval support to end users .Problem Statement
  7. 7.  The ontologies reviewed are as follows: Vocabularies and Retrieval Tools in Biomedicine:Vanopstal, Robert (2011) The OntoSeed ontology (2007)Creating Ontologies for Content Representation The RiboWeb ontology for Ribosome (2003)Semantic Web for biological sciences 7Literature Review
  8. 8. Semantic Web for biological sciences 8Literature ReviewOntoEdit: Guiding Ontology Development byMethodology and Inferencing. (2009) In: R. Meersman,Z. Tari et al. (eds.) Proceedings of the ConfederatedInternational Conferences, University of California,Methodology for Development and Employment ofOntology Based Knowledge Management Applications:York Sure, (2005) KAON - Towards a large scale Semantic Web. E.Bozsak, M. Ehrig, S. Handschuh et al. Proceedings ofEC-Web (in combination with DEXA2002).
  9. 9. Semantic Web for biological sciences 9Motivation It is an effort to conceptualize a biologicalknowledge base for biologist, scientist, end-users that aim to retrieve biologicalinformation at web scale. Semantic data model give solutions in suchdomain. In general it has a wider applicability thanrelational or object oriented databases.
  10. 10. Developing Semantic WebApplicationSemantic Web for biological sciences 10 “Semantic Web Applications usually make someontological commitments. They need to have hard-coded knowledge aboutdomain ontology obtained from experts whichcontains classes i.e., plants, animals, angiospermgymnosperm along with their relations &associations . The application can also operate on extensions ofthese core concepts e.g., stemming from dynamicextension ontologies about specific types.
  11. 11. Developing Semantic Web ApplicationSemantic Web for biological sciences 11 A Semantic Web Application is still an applicationresearch work, thus it needs to follow good practicefrom Software Engineering. Spiral Model inspired by the famous Boehm spiral isused in this application development.It is an extensible search framework for semantic webapplications.
  12. 12. Semantic Web Application ModelSemantic Web for biological sciences 12
  13. 13. Semantic Web for biological sciences 13Semantic Web Architecture the XML layer, whichrepresents data the RDF layer, whichrepresents the meaning ofdata the Ontology layer, whichrepresents the formal rulescommon agreement aboutmeaning of data. the Logic layer, whichenables intelligent reasoningwith meaningful data.
  14. 14. Semantic Web for biological sciences 14Methodology & Procedures In order to built conceptual data model.There is a need to clearly state the elementsthat are abstracted. These elements are concepts, properties ofconcepts, relations and properties ofrelations. The meaning of each relation between twoconcepts must be established. It allows semantics in applications toautomatically derive information.
  15. 15. Semantic Web for biological sciences 15Methodology & Procedures Table 1. Definitions and Examples of RelationsRelations Definitions ExamplesC is-a C1 Every C at any time is atthe same time a C1Apple is-a FruitLion is-a AnimalC part-of C1 Every C at any time ispart of some C1 at thesame timeHeart part-of BodySeeds part-of Fruit
  16. 16. Semantic Web for biological sciences 16Methodology & Procedures Table 2. Algebraic Properties of RelationsRelations Transitive Symmetric Reflexiveis-a + - +part-of + - +
  17. 17. Semantic Web for biological sciences 17Methodology & Procedures Semantic Web Application Using Jena a java framework To access and manipulate RDF data model The object model offers methods to retrieve concepts object-properties subject-properties ,etc the rest is conventional programming…
  18. 18. Ontology Development ProcessSemantic Web for biological sciences 18
  19. 19. Semantic Web for biological sciences 19 Domain Ontology Screen Shot
  20. 20. Semantic Web for biological sciences 20 Domain Ontology Screen Shot
  21. 21. Semantic Web for biological sciences 21 Domain Ontology Screen Shot
  22. 22. Semantic Web for biological sciences 22Thank you