Food Informatics: Sharing Food Knowledge for Research & Development

1. Food Informatics: Sharing FoodKnowledge for Research & Development Nicole Koenderink, Lars Hulzebos, Hajo Rijgersberg, Jan Top Nicole.Koenderink@wur.nl Agrotechnology & Food Innovations Wageningen UR, The Netherlands

2. Custard Why does custard taste so creamy? Bite size Colour Odour Movement of tongue Oral texture Percentage of fat particles Perception of thickness Temperature Amount of saliva AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 2

3. Outline • Problem & Purpose • Approach • First Results • Conclusion & Future Work • Problem & Purpose AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 3

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 4

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 5

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 6

7. Outline • Problem & Purpose • Approach • First Results • Conclusion & Future Work • Approach AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 7

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 8

9. Interviews, Oral K.A. Textmining automation 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 9

10. Approach (1) Goaldefinition AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 10

11. Approach (2) Searchpotentialrelevanttriples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 11

12. Approach (3) & (6) Potentialrelevanttriples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 12

13. Approach (4) Search new information AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 13

14. Approach (5) Parsedtriples AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 14

15. Outline • Problem & Purpose • Approach • First Results • Conclusion & Future Work • First Results AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 15

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 16

17. IARC thesaurus • USDA thesaurus • CARAT thesaurus • www.bulkfoods.com • Unilever triples Total: 651640 triples First Results • Triple collection filled with • CABS thesaurus • NALT thesaurus • AGCOM thesaurus • Total amount of triples (May): approx. 350,000 AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 17

18. First Results 6th AOS Workshop - Use of Ontologies in Applications 18

19. First Results

20. First Results

21. First Results

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 23

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 24

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 25

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 26

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? Nicole.Koenderink@wur.nl AOS Workshop - Use of Ontologies in Applications – Nicole Koenderink 27

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> 1 1* 1* <OBJECT> <PREDICATE> <SUBJECT>

31. Parsing triples – Example