Food Informatics-Sharing Food
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Transcript of Food Informatics-Sharing Food
Food Informatics: Sharing FoodKnowledge for Research & Development
Nicole Koenderink, Lars Hulzebos, Hajo Rijgersberg, Jan Top
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
6th AOS Workshop - Use of Ontologies in Applications 18
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?
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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