1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tracking Referents (based on OIC, December 1, 2006) Barry SMITH and Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representational artifacts classified according to the sort of entities they are about Non-FormalizedFormalized Primarily about particulars news reportsinventories, referent tracking database Primarily about universals / types scientific theories, textbooks ontologies, terminologies,

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world The world consists of entities that can be divided according to three dichotomies –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; –together with relations between these entities e.g. is-instance-of, e.g. is-member-of e.g. is_a (is-subtype-of)

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world (1) air plane philosopher airport universals/types instances/particulars Enola Gay Barry Smith JFK George Bush instance of president

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world (2) Enola Gay Barry Smith JFK George Bush t continuants flying meeting occurrents

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world (3) philosopher universals particulars Barry Smith George Bush presidentchildadult t Instance-at t

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Inadequate representational units “JFK”“Enola Gay” “Barry Smith”“George Bush”

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Purpose: –explicit reference to the concrete individual entities relevant to the accurate description of a scene Proposed Solution: Referent Tracking Now! That should clear up a few things around here ! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 78 Numbers instead of words Method: –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Essentials of Referent Tracking generate of universally unique identifiers; decide what particulars should receive a IUI; finding out whether or not a particular has already been assigned a IUI (each particular should receive maximally one IUI); using IUIs to make statements; determining the truth values of statements in which IUIs are used; correcting errors in the assignment of IUIs.

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U IUI generation Universally Unique IDs: –recently standardized through ISO/IEC 9834-8:2004, –specifies format and generation rules enabling users to produce 128-bit identifiers that are either guaranteed or have a high probability of being globally unique –Meaningless strings –Central management or certification not needed to guarantee uniqueness (But use as IUI requires this)

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U IUI assignment = an act carried out by the first ‘cognitive agent’ who recognizes the need to acknowledge the existence of a particular it has information about by labeling it with a IUI. ‘cognitive agent’: –A person; –An organisation; –A device or software agent, e.g. Bank note printer Image analysis software

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Criteria for IUI assignment (1) 1.Different for continuants and for occurrents 2.The continuant is in front of you, you can see it, photograph it –The photograph gets a IUI; your act (occurrent) of taking the photo gets a IUI 3.The occurrent occurs in your presence, you can make a video –The video gets a IUI; your act (occurrent) of taking the video gets a IUI 4.When assigning a IUI you may not know exactly what the particular is (which type it instantiates)

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Criteria for IUI assignment (2) 2.The particular’s existence ‘may not already have been determined as the existence of something else’: Morning star and evening star Himalaya  2 observers not knowing they observed the same thing 3.May not have already been assigned a IUI. 4.It must be relevant to do so: Personal decision, (scientific) community guideline,... Possibilities offered by the EHR system If a IUI has been assigned by somebody, everybody else making statements about the particular should use it

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assertion of assignments IUI assignment is an act whose \execution has to be asserted in the IUI-repository: – d a IUI of the registering agent A i the assertion of the assignment »p a IUI of the author of the assertion »p p IUI of the particular »t ap time of the assignment »coptional description for identification t d time of registering A i in the IUI-repository Neither t d or t ap give any information about when #p p started to exist. This might be asserted in statements providing information about #p p.

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U PTP statements - particular to particular ordered sextuples of the form s a is the IUI of the author of the statement, t a a reference to the time when the statement is made, r a reference to a relationship (available in o) obtaining between the particulars referred to in P, o a reference to the ontology from which r is taken, P an ordered list of IUIs referring to the particulars between which r obtains, and, t r a reference to the time at which the relationship obtains. P contains as many IUIs as required by the arity of r. In most cases, P will be an ordered pair such that r obtains between the particular represented by the first IUI and the one referred to by the second IUI. As with A statements, these statements must also be accompanied by a meta-statement capturing when the sextuple became available to the referent tracking system.

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U PTCL statements – particular to class s a is the IUI of the author of the statement, t a a reference to the time when the statement is made, inst a reference to an instance relationship available in o obtaining between p and cl, o a reference to the ontology from which inst and cl are taken, p the IUI referring to the particular whose inst relationship with cl is asserted, cl the class in o to which p enjoys the inst relationship, and, t r a reference to the time at which the relationship obtains.

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Other Advantages mapping as by-product of tracking –Descriptions about the same particular using different ontologies/concept-based systems Quality control of ontologies and concept-based systems –Systematic “inconsistent” descriptions in or cross terminologies may indicate poor definition of the respective terms

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dynamic aspects

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Accept that everything may change: 1.changes in the underlying reality: Particulars and universals come and go 2.changes in our (scientific) understanding: The plant Vulcan does not exist 3.reassessments of what is considered to be relevant for inclusion (notion of purpose). 4.encoding mistakes introduced during data entry or ontology development.

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality versus beliefs, both in evolution IUI-#3 O-#2 O-#1 t U1 U2 p3 Reality Belief O-#0 = “denotes” = what constitutes the meaning of representational units …. Therefore: O-#0 is meaningless

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U An “optimal” representational artifact (2) Each representational unit in such a representational artifact would designate –(1) a single portion of reality (POR), which is –(2) relevant to its purposes and such that –(3) the authors intended to use this representational unit to designate this POR, and –(4) there would be no PORs objectively relevant to these purposes that are not referred to in the representational artifact.

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sources of error assertion errors: sources may be in error as to what is the case in their target domain; relevance errors: sources and analysts may be in error as to what is objectively relevant to a given purpose; encoding errors: they may not successfully encode their underlying cognitive representations, so that particular representational units fail to point to the intended PORs.

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key requirement for updating Any change in an ontology or data repository should be associated with the reason for that change to be able to assess later what kind of mistake has been made !

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: a person’s gender In John Smith’s EHR: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake (was understood as male, but wrongly transcribed)

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realism-based metric for data quality Must be able to deal with a variety of problems by which matching endeavors thus far have been affected –different authors may have different though still veridical views on the same portion of reality, –authors may make mistakes, when interpreting reality, or when formulating their interpretations in their chosen representation language –a matcher can never be sure to what the expressions in an repository actually refer (no God’s eye perspective), –if two ontologies are developed at different times, reality itself may have changed in the intervening period.

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U An example: merging data from two sources Reality exist before any observation R And also most structures in reality are there in advance

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The author of O 1 acknowledges the existence of some Portion Of Reality (POR) R B1B1 Some portions of reality escape his attention.

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U R He considers only some of them relevant for O 1, represents thus only part, here with Int = R+. O1O1 B1B1 #1 RU 1 B1 RU 1 O1 Both RU 1 B1 and RU 1 O1 are representational units referring to #1; RU 1 O1 is NOT a representation of RU 1 B1 ; RU 1 O1 is created through concretization of RU 1 B1 in some medium.

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U R Similarly concerning the author of O 2 O1O1 B2B2 B1B1 O2O2

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U R Creation of the mapping O1O1 B2B2 B1B1 O2O2 OmOm

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two (out of many other) possible configurations #1 was not considered to be relevant for O 2, but is considered to be relevant for O m. The author of O 1 made an encoding mistake, so that his ontology contains a reference to a non-intended referent, and this is copied into O m.

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Typology of expressions included in and excluded from an ontology in light of relevance and relation to external reality

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Typology of expressions included in and excluded from an ontology in light of relevance and relation to external reality Valid presence in the representation Valid absence in the representation

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Typology of expressions included in and excluded from an ontology in light of relevance and relation to external reality Unjustified presence in the representation Unjustified absence in the representation But sometimes you get lucky …

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The original beliefs are usually not accessible R O1O1 O2O2 B2B2 B1B1 OmOm

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The original beliefs are usually not accessible R O1O1 O2O2 OmOm But if the ontologies are well documented and representations intelligible, then many such beliefs can be inferred, and mistakes found.

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U For concept-based systems, there is also no reality R O1O1 O2O2 OmOm

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U But that what must hold if both ontologies are believed to be right, can be believed to mirror reality O1O1 OmOm O2O2

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The principle of forced backward belief A lot of information loss

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A decision support tool for dealing with inconsistencies ? O 1 : –Holds that penguins are birds, birds fly O 2 : –Holds that penguins are birds, penguins don’t fly The problem for O m : –Which source ontology to believe? –What might be the source of the inconsistency ? O 1 is right and penguins do fly O 1 is wrong and either penguins are not birds or not all birds fly Both are right but the representational units ‘penguin’, ‘bird’ and ‘fly’ do not refer to the same entities in reality.

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible evolutions through updates

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible evolutions through updates Example: a relevant entity ceases to exist, but the representation is not updated:

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Updating is an active process authors assume in good faith that –all included representational units are of the P+1 type, and –all they are aware of, but not included, of A+1 or A+2. If they become aware of a mistake, they make a change under the assumption that their changes are also towards the P+1, A+1, or A+2 cases. Thus at that time, they know of what type the previous entry must of have been under the belief what the current one is, and the reason for the change.

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U This leads to a calculus … NOT: –to demonstrate how good an individual version of an ontology is, But rather –to measure how much it improved (hopefully) as compared to its predecessors. Principle: recursive belief revision

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Beliefs At t about t Backward belief revision over time Reality: a POR exists and is not relevant At time t, an analyst correctly perceives the existence of some particular, but considers it relevant while it isn’t, and he makes an encoding error such that the representational unit does not refer. There is thus a -2 error with respect to reality, but this remains, of course, unknown. -2 R P

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Beliefs At t about t Backward belief revision over time At t+1 about t+1 At t+1 about t Reality: a POR exists and is not relevant At t+1, he correct the encoding mistake, which forces him to believe that at t, the unit-reality configuration was of type P-4 rather than P+1. R P -2

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Beliefs At t about t Backward belief revision over time At t+1 about t+1 At t+1 about t Reality: a POR exists and is not relevant Although he believes that the current situation is P+1, it is in reality P-6, where it was P-7 before. The real error is now -1, while the perceived error with respect to t is also -1 R P -2

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Beliefs At t about t Backward belief revision over time At t+1 about t+1 At t+1 about t Reality: a POR exists and is not relevant At t+2, he believes that the posited POR in fact does not exist R P -2

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Beliefs At t about t Backward belief revision over time At t+1 about t+1 At t+1 about t Reality: a POR exists and is not relevant At t+2 about t+2 At t+2 about t+1 At t+2 about t R P -2 -3 -5

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion Realist ontology is a powerful quality assurance tool for building high quality ontologies AND high quality databases; Referent tracking, based on realist ontology, is a means to remove the ambiguity in data that cannot be solved by realist ontology alone; –It is a form of “adult” annotation Application of RT requires a globally accessible repository The use of “meaningless” IUIs allows very strict safety and security measures to be implemented.