The document discusses developing food ontologies to enable knowledge sharing in food research and development. It presents an approach to semi-automatically acquire knowledge from text sources for inclusion in ontologies. The approach involves defining the ontology's purpose, searching text for potentially relevant concepts and relationships, and having experts validate relevance. An initial case study developed an ontology of food ingredients with over 3,000 concepts within 4 hours using this approach. Future work focuses on improving expert validation and transforming acquired knowledge into formal ontologies.

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

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

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

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