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  • fyisisch, chemisch, sensorisch
  • Much food-related knowledge is available in various research fields Much food-related knowledge is available in various research fields
  • Research: Wageningen UR, TNO, Vrije Universiteit Amsterdam, University of Amsterdam IT partners: IBM Business: Unilever, Friesland Foods

Transcript

  • 1. Food Informatics: Sharing Food Knowledge for Research & Development Nicole Koenderink , Lars Hulzebos, Hajo Rijgersberg, Jan Top [email_address] Agrotechnology & Food Innovations Wageningen UR, The Netherlands
  • 2. Custard Why does custard taste so creamy? AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink Movement of tongue Percentage of fat particles Bite size Oral texture Perception of thickness Temperature Colour Odour Amount of saliva
  • 3. Outline
    • Problem & Purpose
    • Approach
    • First Results
    • Conclusion & Future Work
    • Problem & Purpose
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 4. Problem & Purpose – Food Informatics
    • Goal: make food-related information available for food researchers.
    • Pay attention to:
      • Relevance
      • Reliability/Quality
      • Timeliness
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 5. Problem & Purpose – Food Informatics
    • Food Informatics: develop tools and technologies to enable application of ontologies for knowledge sharing
    • Collaboration between:
      • Research
      • IT partners
      • Business
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 6. Problem & Purpose – Food Informatics However…. only few ontologies exist dedicated to the field of food.
    • Our first purpose:
    • collect “structured” knowledge on the field of food
    • support users in creating relevant food ontologies
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 7. Outline
    • Problem & Purpose
    • Approach
    • First Results
    • Conclusion & Future Work
    • Approach
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 8. Approach – relevant knowledge
    • Ontology contains domain knowledge
    • Without defined purpose it is impossible to determine which knowledge is relevant and thus which knowledge should be added to ontology
    • Traditionally: (purpose) independent representation of domain knowledge
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 9. Approach – knowledge acquisition
    • Complete oral K.A. process:
    • Tedious & time-consuming for expert
    • Complete text mining process:
    • Too generic for purpose-oriented ontology
    Our approach AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink Interviews, Oral K.A. Text mining automation
  • 10. Approach (1) Goal definition AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 11. Approach (2) Search potential relevant triples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 12. Approach (3) & (6) Potential relevant triples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 13. Approach (4) Search new information AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 14. Approach (5) Parsed triples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 15. Outline
    • Problem & Purpose
    • Approach
    • First Results
    • Conclusion & Future Work
    • First Results
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 16. First Results
    • Case study: Research Management System catalogue food according to properties of ingredients
    • Needed: ontology of food ingredients
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 17.
    • Triple collection filled with
      • CABS thesaurus
      • NALT thesaurus
      • AGCOM thesaurus
    • Total amount of triples (May): approx. 350,000
    First Results AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink Total: 651640 triples
    • IARC thesaurus
    • USDA thesaurus
    • CARAT thesaurus
    • www.bulkfoods.com
    • Unilever triples
  • 18. First Results 6th AOS Workshop - Use of Ontologies in Applications
  • 19. First Results
  • 20. First Results
  • 21. First Results - 0 0 30,796 12 50% 3 6 30,791 11 13% 8 62 30,764 10 24% 36 152 30,523 9 28% 150 532 29,783 8 17% 392 2,274 27,183 7 27% 775 2,831 19,660 6 52% 1,001 1,934 9,548 5 55% 552 1,004 3,505 4 57% 182 319 885 3 67% 55 83 181 2 100% 7 7 - 1 % relevant new concepts # of relevant new concepts # of new concepts cumulative # proposed triples step
  • 22. First Results
    • Result: basis for ontology with 3150 concepts within 4 hours
    • Number of relations per concept varies
  • 23. Conclusions
    • Purpose is necessary to define relevant knowledge; ontology is purpose-dependent.
    • With the proposed semi-automatic knowledge acquisition method, the expert decides which knowledge is relevant
    • Observation: it is difficult for an expert to stay focused on the objective of the ontology.
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 24. Conclusions
    • The proposed two-step approach has as advantage that in a short period many possibly relevant concepts are indicated
    • A drawback of this method is that the expert has to assess each time a huge amount of triples
    • Future work: the method needs a “filter routine” to assist the expert in this process.
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 25. Conclusions
    • The relations in the thesaurus are general
    • Future work: the expert must be enabled to redefine relations
      • Example: potato starch is related to potato
      • is changed to
      • potato starch is made from potato
      • or
      • potato starch is substance of potato
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 26. Future Work
    • Design filter routine
    • Implement redefinition support
    • Expand the triple collection with triples obtained from less structured documents
    • Next step: transform the found collection of concepts and relations to an ontology
    AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 27. Acknowledgements
    • Thanks to:
    • Jannie van Beek
    • Remco van Brakel
    • the Dutch Ministry of Education, Culture and Science
    • the Dutch Ministry of Economic Affairs
    • the Ministry of Agriculture
    Questions? [email_address] AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink
  • 28.  
  • 29. Parsing triples – Example adoption UF: product introduction NT: adoption behaviour adoption process adoption behaviour BT: adoption behaviour adoption process BT: adoption
  • 30. Parsing triples – Example <TERM> := [A-z]1* <RELATION> := [A-z]1* + “:” <BLANK> := empty line <TERM> [ <RELATION> [ <TERM>]1* ]1* <BLANK> <OBJECT> <PREDICATE> <SUBJECT> 1 1* 1*
  • 31. Parsing triples – Example adoption behaviour BT adoption adoption NT adoption process adoption NT adoption behaviour adoption UF product introduction Object Predicate Subject