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

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

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