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13 feb 20_slides_r_kruiper

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Slides for the presentation on Computer-Aided Biomimetics, presented at the UK-KTN Special Interest Group in Natured Inspired Solutions on the 13th of February at the Royal Society of Edinburgh.

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13 feb 20_slides_r_kruiper

  1. 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. 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. 3. Problem space 3
  4. 4. ● Hardly ever trained as biologists ● Incorporate more specific properties ● Increase diversity of analogies 4 Problem space
  5. 5. ? ● Hardly ever trained as biologists ● Incorporate more specific properties ● Increase diversity of analogies 5 Problem space
  6. 6. 6 Solution space
  7. 7. 7 Solution space
  8. 8. Trade-offs 8 Solution space
  9. 9. Trade-offs 9 Solution space 1. Provide bridge between domains 2. Improve understandability
  10. 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. 11 Trade-offs Complexity of information Accessibility of information (for novice biologist)
  12. 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. 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. 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. 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. 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. 17 Complexity of information Accessibility of information (for novice biologist) TRIZ
  18. 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. 19 Complexity of information Accessibility of information (for novice biologist) Inventive Principle (10): Preliminary Action Solution space
  20. 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. 21. Inventive Principle (10): Preliminary Action Complexity of information Accessibility of information (for novice biologist) 21 AskNature Database IDEA Inspire IBID Solution space
  22. 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. 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. 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. 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. 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. 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. 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. 29 Ontology Databasevs Classes
  30. 30. 30 Ontology Databasevs Classes Instances
  31. 31. 31 Ontology Databasevs Classes Instances
  32. 32. 32 Ontology Databasevs Classes Instances
  33. 33. 33 Ontology Databasevs
  34. 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. 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. 36 Tube Animal Piercing BioMimetic Ontology (BMO)
  37. 37. 37 TRIZ? Matrix: 10,35,21,16
  38. 38. 38 What about natural solutions?
  39. 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. 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. 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. 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. 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. 44 Rhyssa persuasoria ovipositor Hymenoptera piercing organ BioMimetic Ontology (BMO)
  45. 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. 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. 47 The FOBIE dataset LREC 2020
  48. 48. 48 Example Best poster award ECI conference on Nature-Inspired Engineering 2019
  49. 49. Trade-off extraction Cluster-based Filtering of text 49 Example Best poster award ECI conference on Nature-Inspired Engineering 2019
  50. 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. 51. ● Expand the ontology (knowledge base) ○ Collaborative effort ● Improve tools ○ Use prototypes in case-studies
  52. 52. Questions? Extra special thanks to: Julian Vincent Ioannis Konstas Marc Desmulliez

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