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 VUB Leerstoel 2009-2010 Theme: Ontology for Ontologies, theory and applications Prevented abortions, absent nipples and other unicorns: the need for realism-based ontology development May 18, 2010; 17h00-19h00 Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels Room D0.08 Prof. Werner CEUSTERS, MD Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences and Department of Psychiatry, University at Buffalo, NY, USA

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 Context of this lecture series Biology Translational Research Defense & Intelligence Pharmacology Pharmacogenomics Performing Arts Linguistics Computational Linguistics Medical Natural Language Understanding Informatics Medicine Knowledge Representation Electronic Health Records Referent Tracking PhilosophyOntology Realism-Based Ontology

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 Today’s topic May 18: Realism-based ontology development –A software engineering perspective –What ontology should be philosophical realism, applied to … … ‘knowledge representation’ –Generic/specific distinction relation with Referent Tracking Referent Tracking PhilosophyOntology Realism-Based Ontology

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 The many faces of “ontologies” (For if you missed the first lecture in this series)

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 The word ‘Ontology’ has two meanings Ontology: the science of what entities exist and how they relate to each other. An ontology: a representation of some domain which –(1) is intelligible to a domain expert, and –(2) is formalized in a way that allows it to support automatic information processing.

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 Within the context of ‘an ontology’, the word ‘domain’ has two meanings For most computer scientists: –A representation of an agreed upon conceptualization about which man and machine can communicate using an agreed upon vocabulary For philosophical ontologists: –A representation of a portion of reality Still allowing for a variety of entities to be recognised by one school and refuted by another one

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 Few embrace both and they should be applauded ‘A message to mapmakers: highways are not painted red, rivers don't have county lines running down the middle, and you can't see contour lines on a mountain.’ William Kent, Data and Reality. First published by North Holland in 1978. Republished in 1998 by 1stBooks. Bill Kent 1936-2005

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 Three types of ontologies Upper level ontologies: –(should) describe the most generic structure of reality Domain ontologies: –(should) describe the portion of reality that is dealt with in some domain –Special case: reference ontologies Application ontologies: –To be used in a specific context and to support some specific application

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 The dispute between … “Practical engineers”: –If it works for our purposes, it is ok Good philosophers: –If it works always, it is ok, and –It can only always work if it represents the relevant portion of reality faithfully.

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 Ontology A computer science and software engineering perspective

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 Ontology (CS/SE) is a –computer-based, –shared, –agreed, –formal, –conceptualization –of a domain.  by distinct software agents  by the developers thereof  no room for misinterpretation  concretization of the cognitive representation of the domain by the authors R. Meersman. Hybrid ontologies in a tr-sortal internet of humans, systems and enterprises. In: B. Smith, R. Mizoguchi, S. Nakagawa. Interdisciplinary Ontology vol.3. Keio University, Tokyo, 2010;43-50.

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 An example: the domain of laser pointers

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 An example: the domain of laser pointers

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 ‘This unique laser pointer projects a red dot’ One could then produce statements (‘triples’) of the sort: But what does this mean? –only this specific pointer (the one from which an image was taken) or all similar pointers of the same model? –does such a laser pointer projects a dot all the time? –is only the red of the dot a subtype of color or is the red of whatever is red a subtype of color ? –do laser pointers only project red dots? –...

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 More esoterically this one does not project a dot

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 More esoterically this one does neither

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 More esoterically now it does, although nothing at all changed in the pointer –projecting a dot does not depend on the pointer only

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 More esoterically now this pointer projects a brown dot

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 More esoterically these two pointers project the very same dot

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 Conclusion Triples of this sort don’t mean a thing !

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 Better: ‘lexons’ in DOGMA Example lexons are: where: –γ: a context identifier (pointing f.i. to the text about laser pointers) –Laser Pointer, Dot, Red, Color: terms corresponding to unique concepts within context γ –projects, projection_of, with_color, color_of: terms corresponding to roles played by the concepts in context γ M. Jarrar, R. Meersman. Formal Ontology Engineering in the DOGMA Approach. Lecture Notes In Computer Science;2519: 1238 – 1254, 2002.

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 ‘This unique laser pointer projects a red dot’ And, in addition, commitments of the sort: –each Dot has at most one Color –each Laser Pointer projects at least one Dot –… M. Jarrar, R. Meersman. Formal Ontology Engineering in the DOGMA Approach. Lecture Notes In Computer Science;2519: 1238 – 1254, 2002.

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 This has advantages Lexons provide some sort of common sense knowledge accessible to humans, Commitments provide an interpretation of lexons, Applications pick and choose what is relevant for their purpose.

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 The DOGMA dogma ‘It is fundamental to realize that this formalism implies that to the application agents, the ontology (i.e. the ontology base (= lexons) plus the agent’s commitment to a part of it) is the real world, nothing more nor less. Lexons in a DOGMA ontology base are always "true", i.e. free of further "interpretation". Alternative truths, or partial ones as typically emerge during the engineering process have to be provided in separate conceptualizations or contexts. Contexts that specify improbable or impossible (contradictory) worlds are possible, especially in the early stages of engineering an ontology, but in practice will have few or no applications that can commit to them.’ M. Jarrar, R. Meersman. Formal Ontology Engineering in the DOGMA Approach. Lecture Notes In Computer Science;2519: 1238 – 1254, 2002.

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 Another (simple) example in DOGMA M. Jarrar, R. Meersman. Formal Ontology Engineering in the DOGMA Approach. Lecture Notes In Computer Science;2519: 1238 – 1254, 2002.

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 The corresponding lexons

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 … and commitments …

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 Crucial questions To which commitments should an application commit? How can application developers judge whether lexons and corresponding commitments make sense? Is the notion of ‘context’ formal enough to be the basis for such decisions?

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 Laser pointers again ‘lexons are assumed (by an outside cognitive agent such as a human understanding that document) to be "true within that context's source“’ M. Jarrar, R. Meersman. Formal Ontology Engineering in the DOGMA Approach. Lecture Notes In Computer Science;2519: 1238 – 1254, 2002.

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 Further questions Do we need now an ontology of contexts of laser pointers ? e.g. – what context goes here? If γ 1 and γ 2 are part of γ 3, then what remains of the ‘unique concepts’,, and ? Can in γ 3 dots have more than one color?

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 But what, after all, should we represent? this or that?Ontology: conceptualization of

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 Ontology: The philosophical realism perspective

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 Laser pointer document Ontology is-about The realist view Laser pointer Ontology is-about ≠

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 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. Basic axioms of philosophical realism Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

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 Three major views on reality Basic questions: –What does a general term such as ‘laser pointer’ refer to? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

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 Dominant view in computer science is conceptualism Basic questions: –What does a general term such as ‘laser pointer’ refer to? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

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 Dominant view in computer science is conceptualism RealismConceptualismNominalism Semantic Triangle concept objectterm Embedded in Terminology

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 ‘Terminology’: one word, two meanings Terminology is the study of identifying and labelling ‘concepts’ pertaining to a subject field. Terminology related activities: –analysing the concepts and concept structures, –identifying the terms assigned to the concepts, –establishing correspondences between terms, possibly in various languages, –compiling a terminology, on paper or in databases, –managing terminology databases, –creating new terms, as required.

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 However … Terminology: –solves certain issues related to language use, i.e. with respect to how we talk about entities in reality (if any); Relations between terms / concepts –does not provide an adequate means to represent independent of use what we talk about, i.e. how reality is structured; Women, Fire and Dangerous Things (Lakoff). Ontology (of the right sort) : –Language and perception neutral view on reality. Relations between entities in first-order reality This is the ‘terminology / ontology divide’

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 Important to differentiate between Lexical, semantic and ontological relations gall gall bladder bladder inflammation urine cystitis biliary cystitis gallbladder inflammation urinary bladder ‘urinary’ ‘gall’ ‘gallbladder ’ ‘urinary bladder’ ‘urinary bladder inflammation’ ‘gallbladder inflammation’ ‘inflammation’ ‘bladder’

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 The semantic triangle revisited concepts termsobjects Representation and Reference First Order Reality about terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about universalsparticulars objects terms concepts cognitive units communicative units representational units

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 Terminology Realist Ontology Representation and Reference First Order Reality universalsparticulars cognitive units representational units (1) Entities with objective existence which are not about anything (2) Cognitive entities which are our beliefs about (1) communicative units (3)Representational units in various forms about (1), (2) or (3) Three levels of reality in Realist Ontology

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 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

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 Reality exist before any observation R

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 Reality exist before any observation Humans had a brain well before they knew they had one. Trees were green before humans started to use the word “green”. R And also most structures in reality are there in advance.

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 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

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 The ontology author acknowledges the existence of some Portion Of Reality (POR) R B

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 R B Some portions of reality escape his attention.

52. 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 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

53. 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 represents only what he considers relevant O B #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.

54. 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 please be aware... These concretizations are NOT supposed to be the representations of these cognitive representations; They are representations of the reality (probably containing mistakes) “concept representation” We should not be in the business of

55. 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 Some characteristics of an optimal ontology Each representational unit in such an ontology would designate –(1) a single portion of reality (POR), which is –(2) relevant to the purposes of the ontology and such that –(3) the authors of the ontology intended to use this unit to designate this POR, and –(4) there would be no PORs objectively relevant to these purposes that are not referred to in the ontology.

56. 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 Basic Formal Ontology (BFO)

57. 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 Aristotle’s Ontological Square SubstantialAccidental Second substance man cat ox Second accident headache sun-tan dread First substance this man this cat this ox First accident this headache this sun-tan this dread Universal Particular

58. 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 Not accepted by all philosophers …

59. 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 And we need more … Holes

60. 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 Aristotle’s Accidental Categories quantum? quantity quale? quality ad quid? relation ubi? place quando? time in quo situ? status/context in quo habitu? habitus quid agit? action quid patitur? passion 60 … can be better organized

61. 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 Basic components of the world: the BFO view The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, lacks e.g. is-member-of, is-part-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

62. 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 BFO – extension of the ontological square

63. 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 example to work (partially) out: ‘walking’ methis walking Has-participant at t 2 human being Instance-of at t living creature Is_a walking Instance-of my left leg part-of at t this leg moving leg moving part-of leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of Has- Participant at t Is-realized- In at t Has-function at t

64. 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 Particulars methis walking my left leg this leg moving to make me walk Individual entities that carry identity and preserve their identity over time 1

65. 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 Universals human being living creature walkingleg moving leg function process Entities which exist “in” the particulars amongst which there is a relation of similarity not found with other particulars 1

66. 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 Particulars versus Universals some particular some universal instanceOf … entities on either site cannot ‘cross’ this boundary every particular is an instance of at least one universal for every universal there is or has been at least one instance

67. 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 Particulars and Universals methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 1

68. 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 instanceOf at t 2 instanceOf at t 1 instanceOf at t 2 The importance of temporal indexing this-1’s stomach benign tumor instanceOf at t 1 this-4 malignant tumor partOf at t 1 stomach partOf at t 2

69. 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 Continuants and Occurrents methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 2

70. 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 Continuants me human being Instance-of at t my left leg leg to make me walk function Instance-of at t Instance-of at t Continuants are entities which endure (=continue to exist) while undergoing different sorts of changes, including changes of place. While they exist, they exist “in total”. 2

71. 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 Continuants preserve identity while changing caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980 2

72. 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 Occurrents this walking walking Instance-of this leg moving leg moving Instance-of Occurrents are changes. Occurrents unfold themselves during temporal phases. At any point in time, they exist only in part. 2

73. 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 Independent versus dependent methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of 3

74. 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 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk 3

75. 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 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk Independent continuants Dependent continuants Occurrents (are all dependent) 3

76. 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 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) 3

77. 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 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) Realizations flexing studying ordering breaking continuantsoccurrents 3

78. 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 Specific Dependence on the instance level a depends_on b =def. a is necessarily such that if b ceases to exist than a ceases to exist on the type level A specifically_depends_on B =def. for every instance a of A, there is some instance b of B such that a depends_on b. 78

79. 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 Generically Dependent Continuants Generically Dependent Continuant Information Object Gene Sequence if one bearer ceases to exist, then the entity can survive, because there are other bearers (copyability) the pdf file on my laptop the DNA (sequence) in this chromosome 79

80. 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 Continuant Independent Continuant Specifically Dependent Continuant Quality Realizable Dependent Continuant (function, role, disposition)

81. 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 BFO Top-Level Ontology (partial) Continuant Occurrent (always dependent on one or more independent continuants) Independent Continuant Dependent Continuant Role Function Realizable Spatial Region Temporal Region Process Quality SDC GDC Disposition Information Content Entity Functioning

82. 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 Relations

83. 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 Unconstrained reasoning OWL-DL reasoning Sorts of relations U1U2 P1 P2 UtoU: isa, partOf, … PtoU: instanceOf, lacks, denotes… PtoP: partOf, denotes, subclassOf,…

84. 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 Universals and Defined Classes I-y DC-x: patients at t class_member_of at t E: all human beings at t class_member_of at t HUMAN BEING instance_of at t extension_of at t subclass_of at t OWL-DL reasoning Unconstrained reasoning

85. 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 There are way more sorts of classes than universals FeverRashTremorEdema Quality Independent continuant Process isa extension_of do not have corresponding universals

86. 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 General principle about relationships All universal level relationships are defined on the basis of particular level relationships

87. 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 Primitive instance-level relationships (RO) c instance_of C at t - a primitive relation between a continuant instance and a class which it instantiates at a specific time p instance_of P - a primitive relation between a process instance and a class which it instantiates holding independently of time c part_of c1 at t - a primitive relation between two continuant instances and a time at which the one is part of the other p part_of p1, r part_of r1 - a primitive relation of parthood, holding independently of time, either between process instances (one a subprocess of the other), or between spatial regions (one a subregion of the other) c located_in r at t - a primitive relation between a continuant instance, a spatial region which it occupies, and a time r adjacent_to r1 - a primitive relation of proximity between two disjoint continuants t earlier t1 - a primitive relation between two times c derives_from c1 - a primitive relation involving two distinct material continuants c and c1 p has_participant c at t - a primitive relation between a process, a continuant, and a time p has_agent c at t - a primitive relation between a process, a continuant and a time at which the continuant is causally active in the process

88. 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 Is_a is defined over instance-of (1) For continuants C is_a C1 = [definition] for all c, t, if c instance_of C at t then c instance_of C1 at t. For occurrents P is_a P1 = [definition] for all p, if p instance_of P then p instance_of P1.

89. 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 Is_a is defined over instance-of (2) human being living creature me universals particulars is_a instance-of at t therefore

90. 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 Is_a is defined over instance-of (3) childadultcaterpillarbutterfly human being living creature animal me More than subset or inclusion ! is_a Instance-of t1t2

91. 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 Transformation Derivation continuation fusion fission

92. 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 Part-of different for continuants and occurrents methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg process Instance-of at t Is_a Instance-of part-of at t part-of

93. 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 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t

94. 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 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t Part-of ?

95. 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 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t Horse legs are not parts of human beings Amputated legs are not parts of human beings ‘Canonical leg is part of canonical human being’, but…, there are (very likely) no such particulars … Part-of ?

96. 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 tt t instanceOf The essential pieces material object spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

97. 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 comparison with the Good Relations Ontology Hepp, Martin: GoodRelations: An Ontology for Describing Products and Services Offers on the Web, Proceedings of the 16th International Conference on Knowledge Engineering and Knowledge Management (EKAW2008), September 29 - October 3, 2008, Acitrezza, Italy, Springer LNCS, Vol. 5268, pp. 332-347.

98. 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 What is it ? The GoodRelations ontology provides the vocabulary for annotating e-commerce offerings (1) to sell, lease, repair, dispose, or maintain commodity products and (2) to provide commodity services. GoodRelations allows describing the relationship between (1) Web resources, (2) offerings made by those Web resources, (3) legal entities, (4) prices, (5) terms and conditions, and the aforementioned ontologies for products and services (6).

99. 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 Cardinality Recommendations For properties, cardinality recommendations are given in the form "propertyName (0..*)". This indicates the recommended range for the number of occurrences of this property for the same subject. The following variants are used: –(0..1): The property is optional and can be attached at most once to the same subject. –(0..*): The property is optional and can be attached multiple times to the same subject. –(1..1): The property is mandatory and should be attached exactly once to the same subject. –(1..*): The property is mandatory and can be attached multiple times tothe same subject. Can this be implemented in OWL ?

100. 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 ‘Instance’ ‘An Actual Product or Service Instance is a single identifiable object or action that creates some increase in utility (in the economic sense) for the individual possessing or using this very object (Product) or for the individual in whose favor this very action is being taken (Service). –Examples: MyThinkpad T60, the pint of beer standing in front of me, my Volkswagen Golf, the haircut that I received or will be receiving at a given date and time.’ ‘An instance of this class represents the legal agent making a particular offering.’

101. 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 Instance, Model, and Class In the products and services domain, we find multiple types of conceptual entities when it comes to describing what is being offered: –First, actual products, like for example my cell phone or a concrete TV set. –Second, certain product makes and models, e.g. the cell phone make and model Sony 1234 or the car model Ford T. There usually exist actual products that are of the respective make and model, but they all have an identity of their own. In particular, they differ in several properties. –Third, classes of actual products that are similar in function or nature, like for example the class “cell phone” which subsumes all actual cell phones.

102. 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 Relation? A Product or Service Property is the type of a characteristic feature of an actual product or service instance. The value may be a quantitative or a qualitative value. In the former case, a Product or Service Property is a ternary relation between a Product or Services Instance, a Unit of Measurement, and this value.

103. 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 Did you pay attention ?

104. 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 Give me examples Continuant Occurrent (always dependent on one or more independent continuants) Independent Continuant Dependent Continuant Role Function Realizable Spatial Region Temporal Region Process Quality SDC GDC Disposition Information Content Entity Functioning

105. 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 What is wrong ? A Maccagnan, M Riva, E Feltrin, B Simionati, T Vardanega, G Valle, N Cannata. Combining ontologies and workflows to design formal protocols for biological laboratories. Automated Experimentation 2010, 2:3 doi:10.1186/1759-4499-2-3 Equipment nor actions are data types!

106. 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 3 serious problems partonomy is not absolutethe origin of something is not a BFO quality a compound activity is not a hypothesis D Qi, RD. King, AL. Hopkins, GRJ. Bickerton, LN. Soldatova. An Ontology for Description of Drug Discovery Investigations. Journal of Integrative Bioinformatics 2010.

107. 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 Anything correct here ? A Valls, K Gibert, D Sánchez and M Batet. Using ontologies for structuring organizational knowledge in Home Care assistance. International Journal of Medical Informatics 79(5), May 2010, 370-387

108. 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 What can we now say more about this ?