Realism, Concepts and Categories Or: how realism can be pragmatically useful for information systems

1. VT

2. Realism, Concepts and CategoriesOr: how realism can be pragmatically useful for information systems • Barry Smith

3. Conceptual Spaces • Great book • Theory of conceptual spaces can be applied not only to concepts • but also to the universals in reality • (concepts of shell shapes • + invariants among shell shapes themselves)

4. PG: I’M A CONCEPTUALIST • There is a reality (an unknowable Ding an sich) • But I am concerned with perceptions, our experiences • The concepts are in our heads • There may be things out there but they are not very useful for us  pragmatic standpoint

5. PG: I’M A CONCEPTUALIST • The concepts are in our heads • There may be universals out there corresponding to our concepts, but they are not very useful for us  pragmatic standpoint • Do we need realism to solve practical problems in the theory of conceptual spaces?

6. PG: I’M A CONCEPTUALIST • The concepts are in our heads • There may be universals out there corresponding to our concepts, but they are not very useful for us  pragmatic standpoint • Do we need realism to solve practical problems in the theory of conceptual spaces?

7. Experiments are a good way to come into contact with reality • Thus it is significant that in describing his experiment with shells PG distinguished between the space of shells in reality and the conceptual space of shells

8. Experiments are a good way to come into contact with reality • But in appealing to experiments in general (by other cognitive scientists) Peter is assuming that these scientists, too, are able to apprehend invariants in reality • -- in children’s responses to stimuli • -- in language uses • -- in shapes and patterns in stimuli themselves (for example pictures of cups, jugs, bowls)

9. In talking about pigeons, • And in giving an argument for his own doctrine of conceptualism, Peter presupposed a distinction between the universals green and mixture-of-blue-and-yellow out there in the world • And also a distinction between pigeons and humans • Therefore Peter is a realist

10. Peter’s response: • I am just pretending to believe that there is such a mind-independent distinction • BUT THEN THIS DEPRIVES HIS ARGUMENT OF ALL RHETORICAL FORCE

11. Peter’s second response: • I believe that there is such a mind-independent distinction only when I’m arguing for this point in my theory, but when I’m arguing for other points in my theory I don’t believe it any more • Indeed I deny it, because I want to remain faithful to my conceptualism

12. PG: I’M A CONCEPTUALIST • But he needs realism about universals to defend his argument • Do we need universals for other (pragmatic) reasons? • Do we need realism to solve practical problems?

13. Medicine • Medical learning • What medical students know • What doctors know • -- highly multi-dimensional concept spaces • What a typical patient knows • -- a lower-dimensional concept space

14. Medicine • What a doctor knows • -- a multi-dimensional concept space • What there is to be known • -- many medical phenomena we just can’t explain • -- an even more highly multi-dimensional invariant space

15. A practical medical problem • How to choose a doctor? • What a doctor knows • vs. • What there is to be known • -- many medical phenomena we just can’t explain

16. PG: I’M A CONCEPTUALIST • But he needs realism to defend his argument • Do we need realism or not to solve practical problems in information systems?

17. PG Axiom: Concept systems have to be learnable • Therefore there is an upper limit on the number of dimensions they can have, and on the topography by which these dimensions are organized

18. Universal Medical Language System • 5 million words • 1.3 million concepts • divided into 135 dimensions corresponding to the UMLS system of semantic types • Computers mean that we can break out of the restraints of learnability • And then we do not break out at random – rather we break out in reflection of the invariants we find in the reality studied by medical scientists

19. Ontology • like cartography • must work with maps at different scales • How fit these maps (conceptual grids) together into a single system?

21. Consider them • as grids • transparent to reality • allowing our directedness towards objects beyond

22. Cartographic Projection

23. object conceptual grids treated always only as mediators towards objects in reality intentionality = the directedness towards objects via conceptual grids

24. concepts intentionality = the directedness towards concepts

25. object conceptual grids treated always only as mediators towards objects in reality intentionality = the directedness towards objects via conceptual grids

26. Intentional directedness • … is effected via conceptual grids • we are able to reach out to the objects themselves because our conceptual grids are transparent

27. Kantianism • = the inability to appreciate the fact that our conceptual grids can be transparent to reality beyond • = Midas touch epistemology

28. there are many compatible map-like partitions • at different scales, • which are all transparent to the reality beyond

29. animal ontology of biological species canary bird fish ostrich ontology of DNA space Universe/Periodic Table

30. animal canary bird fish ostrich Universe/Periodic Table both are transparent partitions of one and the same reality

31. Ontological Zooming

32. The job of the ontologist • is to understand how different partitions of the same reality interrelate

33. Why Neokantianism makes for bad information systems ontologies • Back to our practical problem

34. IFOMIS • Institute for Formal Ontology • and Medical Information Science • http://ifomis.de

35. The problem • Different communities of medical researchers use different and often incompatible category systems in expressing the results of their work

36. Example: Medical Nomenclature • UMLS: • blood is a tissue • MeSH: • blood is a body fluid

37. different concept systems

38. need not interconnect at all for example they may relate to entities of different granularity

39. we cannot make incompatible terminology-systems interconnect just by looking at concepts, or knowledge or language

40. to decide which of a plurality of competing definitions to accept we need some tertium quid

41. we need, in other words, to take the world itself into account

42. For medical students: patients are the solution • In information systems ontology is the solution’

43. Two alternative readings • Ontologies are special sorts of terminology systems = currently popular IT conception, with roots in KR • Ontologies are special sorts of theories about entities in reality = traditional philosophical conception, embraced by IFOMIS

44. Example: The Gene Ontology (GO) • hormone ; GO:0005179 • %digestive hormone ; GO:0046659 • %peptide hormone ; GO:0005180 %adrenocorticotropin ; GO:0017043 %glycopeptide hormone ; GO:0005181 %follicle-stimulating hormone ; GO:0016913 • % = subsumption (lower term is_a higher term)

45. as tree • hormone • digestive hormone peptide hormone • adrenocorticotropin glycopeptide hormone • follicle-stimulating hormone

46. GO • is very useful for purposes of standardization in the reporting of genetic information • but it is not much more than a telephone directory of standardized designations organized into hierarchies

47. GO • can in practice be used only by trained biologists • whether a GO-term stands in the subsumption relationship depends on the context in which the term is used • (for example on the type of organism)

48. A still more important problem: • GDB • Genome Database of Human Genome Project • GenBank • National Center for Biotechnology Information, Washington DC • etc.

49. What is a gene? • GDB: a gene is a DNA fragment that can be transcribed and translated into a protein • GenBank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype • GO uses ‘gene’ in its term hierarchy, • but it does not tell us which of these definitions is correct

50. GO • has no robust formal organization • no capability to be aligned with systems which would have the power to use it to reason with genetic information