1. Ruben Kruiper
Ioannis Konstas
Marc Desmulliez
Jessica Chen-Burger
Julian Vincent
Rupert Soar
NIMCNature Inspired Manufacturing Centre
Trade-offs for
Computer-Aided Biomimetics
1

2. ● Problem and solution
● Ingredient 1: Trade-offs
● Ingredient 2: Ontology vs Database
● BioMimetic Ontology (BMO)
● Natural Language Processing (NLP)
○ The FOBIE dataset
○ Example
2

3. Problem space
3

4. ● Hardly ever trained as biologists
● Incorporate more
specific properties
● Increase diversity
of analogies
4
Problem space

5. ?
● Hardly ever trained as biologists
● Incorporate more
specific properties
● Increase diversity
of analogies
5
Problem space

6. 6
Solution space

7. 7
Solution space

8. Trade-offs
8
Solution space

9. Trade-offs
9
Solution space
1. Provide bridge between domains
2. Improve understandability

10. Trade-offs● Problem and solution
● Ingredient 1: Trade-offs
● Ingredient 2: Ontology vs Database
● BioMimetic Ontology (BMO)
● Natural Language Processing (NLP)
○ The FOBIE dataset
○ Example
10

11. 11
Trade-offs
Complexity
of information
Accessibility of information
(for novice biologist)

12. 12
Trade-offs
Complexity
of information
Accessibility of information
(for novice biologist)
Can be
introduced
directly
Builds on
fundamental
knowledge
Pythagoras theorem
Quantum Field Theory

13. 13
Trade-offs
Complexity
of information
Accessibility of information
(for novice biologist)
Can be
introduced
directly
Builds on
fundamental
knowledge
Pythagoras theorem
Quantum Field Theory
Focused
introduction to
fundamental
knowledge
Teacher versus self-study

14. 14
Trade-offs (in biology)
Adapted from Agrawal et al. (2010) Tradeoffs and Negative Correlations in
Evolutionary Ecology Why Are We Interested in Tradeoffs?

15. Trade-offs (in engineering)
15
Engineering: TRIZ design methodology
Problem space
Determine relevant abstract
solution principles
Classify as abstract
Trade-off
(contradicting parameters)
Solution space

16. ● Trade-offs: two (or more) conflicting traits of interest
● Can be used to define a problem space
16
Trade-offs
TRIZ example of our problem space?

17. 17
Complexity
of information
Accessibility of information
(for novice biologist)
TRIZ

18. 18
TRIZ
Complexity
of information
= Complexity of control
Accessibility of information
(for novice biologist)
= waste of time 10 - Preliminary action
18 - Mechanical vibration
28 - Replace a mechanical
system
32 - Optical changes
18,28,32,10

19. 19
Complexity
of information
Accessibility of information
(for novice biologist)
Inventive Principle (10):
Preliminary Action
Solution space

20. Inventive Principle (10):
Preliminary Action
Database of biomimetic knowledge?
(Preliminary analysis)
Complexity
of information
Accessibility of information
(for novice biologist)
20
Solution space

21. Inventive Principle (10):
Preliminary Action
Complexity
of information
Accessibility of information
(for novice biologist)
21
AskNature Database
IDEA Inspire
IBID
Solution space

22. Inventive Principle (10):
Preliminary Action
● Time intensive
● Loss of information
● Databases are limited in
scope
Complexity
of information
Accessibility of information
(for novice biologist)
22
Solution space
AskNature Database
IDEA Inspire
IBID

23. Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
Complexity
of information
Accessibility of information
(for novice biologist)
Inventive Principle (10):
Preliminary Action
23
Solution space

24. Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
● Insight into relations and
concepts
Complexity
of information
Accessibility of information
(for novice biologist)
Inventive Principle (10):
Preliminary Action
24
Solution space

25. Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
● Insight into relations and
concepts
● Reuse and share knowledge
(ontology)
Complexity
of information
Accessibility of information
(for novice biologist)
Inventive Principle (10):
Preliminary Action
25
Solution space

26. Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
● Insight into relations and
concepts
● Reuse and share knowledge
(ontology)
Complexity
of information
Accessibility of information
(for novice biologist)
Inventive Principle (10):
Preliminary Action
26
== our solution spaceSolution space
1. Provide bridge between domains
2. Improve understandability

27. ● Problem and solution
● Ingredient 1: Trade-offs
● Ingredient 2: Ontology vs Database
● BioMimetic Ontology (BMO)
● Natural Language Processing (NLP)
○ The FOBIE dataset
○ Example
Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
● Insight into relations and
concepts
● Reuse and share
knowledge (ontology)
27
our solution space
1. Provide bridge between domains
2. Improve understandability

28. 28
Ontology Databasevs
1. Constant evolution. Ontologies stores allow agile schema management
during application runtime, which is supported by the graph-based data model,
in contrast to relational databases.
2. Communication. An ontology enables communication between (i) implemented computational systems, (ii) between humans,
and (iii) between humans and implemented computational systems
3. Inference. An ontology enables computational inference, which is useful for deriving implicit facts; class hierarchy,
classification of instances, consistency checking within an ontology, ...
4. Knowledge organization. Domain analysis is necessary to make domain assumptions explicit and to share an
understanding of the information structure. Ontologies are also means of structuring and organizing knowledge, not only data
5. Reusability. Ontologies enable, on the one hand, reuse of domain knowledge and, on the other, integration of a new
knowledge caucus built upon existing knowledge.
6. T-Box/A-Box separation. Ontologies clearly separate between an ontological schema and its instances.
7. Standardization aims for a uniform language that enables protocols. The implicit bootstrapping problem is that everyone
must agree to an initial lingua franca in order to be able to standardize around it.
8. Identification. A unique identifier, for example, the Internationalized Resource Identifier (IRI) concept, uniquely identifies the
meaning of concepts in a given domain of interest. IRIs enable cross-ontology references, which support reuse and interoperability
between ontologies. Furthermore, the existence of IRIs allows reification – tying concepts to physical items or real-world concepts
(important for business applications).
8 benefits of using ontologies, adapted from Feilmayr and Wöß (2016)

29. 29
Ontology Databasevs
Classes

30. 30
Ontology Databasevs
Classes
Instances

31. 31
Ontology Databasevs
Classes
Instances

32. 32
Ontology Databasevs
Classes
Instances

33. 33
Ontology Databasevs

34. ● Problem and solution
● Ingredient 1: Trade-offs
● Ingredient 2: Ontology vs Database
● BioMimetic Ontology (BMO)
● Natural Language Processing (NLP)
○ The FOBIE dataset
○ Example
Improve access to scientific
biological texts;
● Search for information
(trade-offs bridge)
● Insight into relations and
concepts
● Reuse and share
knowledge (ontology)
34
our solution space

35. 35
BioMimetic Ontology (BMO)
What if Ferdinando Rodriguez y Baena and Julian never met?
Definition needle:
● a small thin piece of steel, with a point at one end and a hole in the other, used
for sewing.
● a very thin, pointed steel tube at the end of a syringe, which is pushed into
your skin to ...

36. 36
Tube
Animal
Piercing
BioMimetic Ontology (BMO)

37. 37
TRIZ?
Matrix:
10,35,21,16

38. 38
What about natural solutions?

39. 39
(At least) 40.000 patents
Classified by contradicting parameters that both required improvement
Determine solution principle per set of contradicting parameters
Result: 40x40 matrix of contradicting parameters and relevant solution principles
Engineering: TRIZ design methodology
BioMimetic Ontology (BMO)

40. Engineering: TRIZ design methodology
40
(At least) 40.000 patents
Classified by contradicting parameters that both required improvement
Determine solution principle per set of contradicting parameters
Result: 40x40 matrix of contradicting parameters and relevant solution principles
Biology research papers
Trade-offs
Determine solution principle
Ontology to retrieve relevant information
Biomimetics
BioMimetic Ontology (BMO)

41. 41
Or specific information
of interest?
ALL of biology
Biological system that matches solution space (trade-off)
e.g. force - stability
Solution principles:
BioMimetic Ontology (BMO)

42. Force: The wasp has a long egg-laying tube that it wants to force into the wood, drilling a
hole as it goes. In order to reduce the force required the tube has a small diameter.
Stability of object: A thin tube is unstable being pushed in to the wood - it will buckle. It is
partially supported by the wasp, but that is not reliable enough.
Resolution: If the tube (or part of it) is under tension it will be stable. The drill pulls itself into
the wood
Megarhyssa nortoni

43. 43
Statement that a (biological) concept …
… is related to
(or has some property,
or is part of some biological process,
or is located somewhere ) …
… as described in a particular reference.
Concept (class) Relation Concept (class) Reference
Rhyssa persuasoria ORDER Hymenoptera 10.1243/09544119JEIM663
Rhyssa persuasoria (female) HAS_ORGAN Ovipositor 10.1243/09544119JEIM663
Ovipositor IS_A Piercing organ 10.1016/j.aspen.2013.04.015
BioMimetic Ontology (BMO)

44. 44
Rhyssa persuasoria
ovipositor
Hymenoptera
piercing organ
BioMimetic Ontology (BMO)

45. 45
Mechanisms of ovipositor insertion and steering
of a parasitic wasp
https://doi.org/10.1073/pnas.1706162114
Functional principles of steerable multi‐element probes in
insects
https://doi.org/10.1111/brv.12467
Functional morphology of the ovipositor in Megarhyssa atrata
(Hymenoptera, Ichneumonidae) and its penetration into wood
https://doi.org/10.1007/s004350050082
BioMimetic Ontology (BMO)

46. ● Problem and solution
● Ingredient 1: Trade-offs
● Ingredient 2: Ontology vs Database
● BioMimetic Ontology (BMO)
● Natural Language Processing (NLP)
○ The FOBIE dataset
○ Example
46

47. 47
The FOBIE dataset LREC 2020

48. 48
Example Best poster award
ECI conference on
Nature-Inspired
Engineering 2019

49. Trade-off extraction Cluster-based
Filtering of text
49
Example Best poster award
ECI conference on
Nature-Inspired
Engineering 2019

50. Trade-off extraction Cluster-based
Filtering of text
50
Example Best poster award
ECI conference on
Nature-Inspired
Engineering 2019
1. Provide bridge
between domains
2. Improve
understandability

51. ● Expand the ontology (knowledge base)
○ Collaborative effort
● Improve tools
○ Use prototypes in case-studies

52. Questions?
Extra special thanks to:
Julian Vincent
Ioannis Konstas
Marc Desmulliez