Politics and Pragmatism in Scientific Ontology Construction

1. Politics and Pragmatismin Scientific Ontology Construction Mike TraversInconsistency Robustness 2011

2. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions

3. My background Artificial IntelligenceKnowledge RepresentationAgent-based systems Programming Languages Media Science Human InterfaceConstructionism Visual Programming • SSS Scientific Software(@startups, large companies, open source projects, and now SRI)Scientific KMCollaborationDecision Support PublishingStandards Philosophy of Science Sociology Cognitive Science Narrative Theory

5. Synopsis • Knowledge representation inevitably involves inconsistency, controversy, hence politics; • Scientific representation does too, but it has worked-out practices for dealing with it; • KR should work more like science rather than the other way around; • Representational Pragmatism: a conceptual framework to make it happen

6. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions

7. What’s a knowledge infrastructure? • A system of • Technologies, • Institutions, • Standards, • and Practices • that serve to support knowledge • Collection • Storage • Curation • Sharing • Validation • …

8. Knowledge Infrastructure #1: Science • The scientific community • An elaborate web of • People (scientists and others) • Institutions (labs, journals, funding agencies, instrument makers…) • Practices (publishing criteria, protocols, conferences) • Works pretty well! The gold standard for knowledge in fact. • But there are issues of scaling, quality, inertia, siloing, epistemological closure…

9. Knowledge Infrastructure #2: The Semantic Web • Set of technical standards for sharing formalized knowledge • Aspires to be a universal framework for knowledge • A grand vision of global-scale knowledge representation • And tremendously important and needed.

10. Provenance Reasoning Classification Relations &Properties Naming

12. These two are becoming one… Bioscience is by far the largest application area for semantic web technology

14. Some non-robust properties of the semantic web • Too inexpressive(Can’t represent default reasoning or n-way predicates) • Too complex(Prevents widespread acceptance) • Too logic-based(Emphasizes wrong things)

15. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions

16. Convergence and Controversy • Ontologies are supposed to define a common understanding of a domain • But “common” is easier said than done • In practice: • Many different constituencies • With different ideas about what’s important • Many side-factors complicate things (implementation cost, personal status, existing non-rigorous usages…) • Compromise is necessary but rarely produces elegant results

17. Example: psychiatric illness • What constitutes a mental illness? • Not at all obvious that categories correspond to real phenomena • Huge changes over over time • Currently defined by DSM-IV through a highly politicized process • History of PTSD (Scott, 1990) • “combat fatigue” or cowardice • In and out of the DSM • Finally recognized as PTSD, partly as response to Vietnam War

18. Psychiatric illness (2) • Homosexuality • Formerly a pathology, now not, through a highly politicized process • Attention Deficit Disorder • Cluster of symptoms, not clear what the boundaries should be • Opinions often determined by theories of child-rearing or institutional aspects of school. • Insurers and economics are important actors in debate • Summary: • these disorders are social constructed categories • over a definite but unclear underlying reality.

19. Example: category fudging • In Pathway Tools, SRI’s bioinformatics knowledge base • This is a widely used system for curating genomes and metabolic pathways • Underlying frame system • Web based interface

21. Example: Gene/Protein conflation • Genes and Proteins are different things • But biologists tend to want to use the same name for a gene and its product • Tension between formal ontology and actual scientific usage • Equivalently, an argument between the computer scientists who build the system and the biologists who use it and curate it

22. Gene (DNA) trpA Gene product(Protein)

23. Search for “trpA”

25. Moral of this somewhat trivial example • There are tensions (inconsistencies) between formal representation and actual usage • And, software makers end up having to cope with these tensions in design decisions • Usually in a kludgy way! • Eg, papering over the conflict in the user interface layer • Would be nice to have a better theory of how do this.

26. Example: how do we classify mitochondria? • Organelles (part of cell) • But descended from separate endosymbiotic organisms • With their own DNA • (Generally but not universally accepted theory)

28. There are consequences • “If we accept that mitochondria are bacteria, then the record books have to be rewritten. The first bacterial genome sequence was completed not by American arriviste Craig Venter …in 1995, but instead by … Fred Sanger, who completed the human mitochondrial genome sequence in 1981!”

29. Expressivity in Description Logics • Description Logics (DL) are the basis for semantic web ontology. • Selected largely for computational tractability • But DL make it hard to do simple things such as representing defaults • All cats have hair • Except for this one! • Expressivity has been traded away • A compromise and perhapsnot the right one

30. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions

31. Bruno Latour • French philosopher and sociologist of science • Roundly reviled for perceived anti-realism • Started with anthropological studies of science in labs and fields • Ends in a rather unique view of representation and even metaphysics

32. Latour for dummies • Science is a social construction (but not an arbitrary one) • Network based: a network consists of humans and non-human actors (lab animals, instruments, funding institutions…) • Agonistic – trials of strength between networks • Understand how science works by tracing the flow of inscriptions, abstractions, and power through these networks • An enriched realism, that provides a rich account of the relation between phenomena and representation

33. Dual face of science Settled science:“That’s the way it is”ObjectiveBlack-boxed Politically Established Natural Science under construction: UnsettledContentiousSearching for allies (people, funding, tools)Building networks of alliance Social

34. Science in the making: • EG: Watson and Crick’s work on the structure of DNA • Speculations (A three-strand model was proposed) • Contending theories • Eventually a winner emerges • Science made • Now that the structure of DNA is known, • it’s a “black box” • we can make instruments that measure it • representations of its sequence

35. Under construction

36. Black boxed

37. Where the representation meets the road • Science is: “the transformation of rats and mice into paper” • Situated representations • From phenomena • Lab notebook • Tables in articles • Laws of nature Concrete, situated Abstract, objective

38. Jeff Shrager, “Diary of an Insane Cell Mechanic”

40. Intercalation of representations and the phenomenon

41. Analogizing to KR Knowledge Representation: Realist Objective Settled Factual Established Abstract Graph structures Knowledge Construction: Situated representations Unsettled Bottom-up User interfaces Ad-hoc structures

42. A new view of the relation between world and representation • Latour refocuses epistemology • Less on the truth of representations, • More on their connection to the world via networks of actants. • Should be a natural fit for computationalists • Who also make systems of symbols with causal connections to the world and each other

43. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions

44. Realism vs Conceptualism • Realism: a movement in philosophy of KR • Led mostly by Barry Smith, SUNY Buffalo(eg “Beyond Concepts: Ontology as Reality Representation”, 2004) • The problem: nobody knows what makes a good ontology • His solution: Aristotelian universals • Bad ontologies are…those whose general terms lack the relation to corresponding universals in reality, and thereby also to corresponding instances. Good ontologies are reality representations...

45. Realism is extremely annoying • Both vacuous and wrong • Vacuous: because it presupposes we know what is real beforehand • Wrong: because it doesn’t correspond to actual scientific knowledge representation • Examples of failure: • Higgs bosons – we don’t know if they are real • Genes – were hypothesized before their “implementation” was known; when were they real? • Software for synthetic chemistry – mixes real and not-yet-real molecular structures

46. Afferent: software for drug discovery chemists

47. But Realism is Winning • Basis of BFO (Basic Formal Ontology) • Which is used by OBO Foundry and other bio-ontology efforts • Nobody wants to be against “realism”… so they picked a good name

48. Realism only deals with half of science • May work for ready-made science, • hopeless for science-in-the-making • Where we don’t know what’s real • And which is where the action is

49. Representational Pragmatism • Needed: a term with good connotations to compete with “realism”. • Connects to a philosophical tradition (James, Peirce, Dewey, Rorty) • “It is astonishing how many philosophical disputes collapse into insignificance the moment that you subject them to this simple test of tracing a concrete consequence” -- James • Bottom-up rather than top-down; opposed to premature ontologizing; Latourian • Support the divergent representational practices of actual science • Help science towards convergence, objectivity, and realism, rather than demanding it upfront.

50. Overview • Introduction • Two kinds of knowledge infrastructure • Ontological controversies: some examples • The nature of actual scientific representation • Representational pragmatism • Technical directions • Conclusions