1. Roberto Marabini Images from: XXVI VAKKI SYMPOSIUM – 10-11-12 February 2006 - Vaasa, Finland

2. An Ontology is…

3. Web search result for: Ontology (17,000,000)

4. The Promise “helpful to communicate information across people and systems…”

5. An Ontology is… Why we are using an ontology

8. Store: 1.Kind of data used as Input/Output 2.Actual values

9. Proposal I: Relational Data Base, one table per program Normalize UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc

10. UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0002.mrc5.3BackgroundImgNor0002.mrc 3Img0003.mrc5.3BackgroundImgNor0003.mrc UIDInputImagesamplingmethodOutImage 1Img0001.mrc5.3BackgroundImgNor0001.mrc 2Img0001.mrc5.3BackgroundImgNor0001_bis.mrc 3Img0003.mrc7.3BackgroundImgNor0003.mrc Proposal I: Relational Data Base, one table per program

11. Normalize Filter … image samplingRate … Normalize, InputFile, image Normalize, OutFile, image Normalize, Sampling, samplingRate … Run_001 Run_002 … InputFile,Img0001.mrc, Run_001 Sampling, 5.6, run_001 …

12. require disjoint … Method, require, radius …

14. hasCompulsoryParameter isa … program normalize … normalize, program, isa…

15. SubjectVerbpredicate NormalizeIsaProgram InputImagehasTypeimage NormalizehascompulsoryAtributeInputImage ….

18. Ontology or not ontology, that is the question.

19. Ontology: focus in 3DEM image processing

20. Technology Persistence layer: EMPIRE (JPA for RDF) -> JENA – SPARQL. https://github.com/mhgrove/Empirehttps://github.com/mhgrove/Empire DataBase: TDB http://incubator.apache.org/jena/documentation/td b/ http://incubator.apache.org/jena/documentation/td b/ Concurrency: Fusseki http://incubator.apache.org/jena/documentation/se rving_data/index.html http://incubator.apache.org/jena/documentation/se rving_data/index.html Conventions web (test)

22. Question

23. Copyright © 2005 Univ. of Manchester23 Coche Automobile Voiture Araba The Car

24. Copyright © 2005 Univ. of Manchester24 Coche Automobile Voiture Araba The Car

25. (A little bit of ontologies) PersonCountry Class (concept) Animal Individual (instance) Belgium Paraguay China Latvia Elvis Hai Holger Kylie S.Claus Rudolph Flipper arrow = relationship label = Property lives_in has_pet

26. Copyright © 2005 Univ. of Manchester26 Pizza Margherita Pizza Vegetarian Pizza Spicy Beef Pizza Pizza Topping Vegetable topping Tomato topping Mozzarella topping Cheese topping Pizza_base Deep dish base Regular base A simple ontology: Pizzas

27. Ontology: focus in 3DEM image processing

28. (A little bit of ontologies) 107 ontologies