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

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

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

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  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?

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

  21. Cartographic Projection

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

  23. concepts intentionality = the directedness towards concepts

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

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

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

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

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

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

  30. Ontological Zooming

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

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

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

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

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

  36. different concept systems

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

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

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

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

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

  42. 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

  43. 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)

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

  45. 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

  46. 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)

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

  48. 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

  49. 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

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