food informatics-sharing food

Food Informatics: Sharing FoodKnowledge for Research & Development

Nicole Koenderink, Lars Hulzebos, Hajo Rijgersberg, Jan Top

Nicole.Koenderink@wur.nl

Agrotechnology & Food InnovationsWageningen UR, The Netherlands

Custard

Why does custard taste so creamy?

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Movement of tongue

Percentage of fat particles

Bite size

Oral texture

Perception of thickness

Temperature

Colour Odour

Amount of saliva

Outline

• Problem & Purpose• Approach• First Results• Conclusion & Future Work

• Problem & Purpose

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Problem & Purpose – Food Informatics

• Goal: make food-related information available for food researchers.

Pay attention to:– Relevance– Reliability/Quality– Timeliness

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Problem & Purpose – Food Informatics

• Food Informatics: develop tools and technologies toenable application of ontologies

forknowledge sharing

• Collaboration between:– Research – IT partners – Business

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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

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Outline

• Problem & Purpose• Approach• First Results• Conclusion & Future Work

• Approach

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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

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Approach – knowledge acquisition

Our approachInterviews,Oral K.A.

Textmining

automation

Complete oral K.A. process:• Tedious & time-consuming for expert

Complete text mining process:• Too generic for purpose-oriented ontology

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Approach

(1) Goaldefinition

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Approach

(2) Search

potentialrelevanttriples

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Approach

(3) & (6) Potentialrelevanttriples

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Approach

(4) Search new information

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Approach

(5) Parsedtriples

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Outline

• Problem & Purpose• Approach• First Results• Conclusion & Future Work

• First Results

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First Results

• Case study: Research Management System catalogue food according to properties

ofingredients

• Needed: ontology of food ingredients

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• Triple collection filled with – CABS thesaurus– NALT thesaurus– AGCOM thesaurus

• Total amount of triples (May): approx. 350,000

First Results

Total: 651640 triples

- IARC thesaurus- USDA thesaurus- CARAT thesaurus- www.bulkfoods.com- Unilever triples

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First Results

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First Results

First Results

First Results

step

cumulative # proposed

triples

# of new

concepts

# of relevant

new concepts

% relevant new

concepts

1 - 7 7 100%

2 181 83 55 67%

3 885 319 182 57%

4 3,505 1,004 552 55%

5 9,548 1,934 1,001 52%

6 19,660 2,831 775 27%

7 27,183 2,274 392 17%

8 29,783 532 150 28%

9 30,523 152 36 24%

10 30,764 62 8 13%

11 30,791 6 3 50%

12 30,796 0 0 -

First Results

• Result: basis for ontology with 3150 concepts within 4 hours

• Number of relations per concept varies

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.

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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.

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Conclusions

• The relations in the thesaurus are general

• Future work: the expert must be enabled to redefine relations

Example: potato starch is related to potatois changed to

potato starch is made from potatoor

potato starch is substance of potato

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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

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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

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Parsing triples – Example

adoptionUF: product introductionNT: adoption behaviour

adoption process

adoption behaviourBT: adoption

behaviour

adoption processBT: adoption

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*

Parsing triples – Example

Subject Predicate Object

product introduction

UF adoption

adoption behaviour

NT adoption

adoption process NT adoption

adoption BT adoption behaviour