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 Department of Philosophy - Spring 2012 Colloquia How to deal with representational artifacts and meanings in Basic Formal Ontology. March 3, 2012 – UB North Campus, Buffalo NY Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences, Ontology Research Group and Department of Psychiatry 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 Language is ambiguous ‘I know that you believe that you understood what you think I said, but I am not sure you realize that what you heard is not what I meant.’ –Robert McCloskey, State Department spokesman (attributed). http://www.quotationspage.com/quotes/Robert_McCloskey/

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 Language is ambiguous Often we can figure it out … in Miami hotel lobby warning on plastic bag in Miami bar

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 Language is ambiguous in Amsterdam hotel elevator Sometimes, we can not …

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 It is worse for machines... “John Doe has a pyogenic granuloma of the left thumb” We see:     The machine sees:

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 It is worse for machines... John Doe pyogenic granuloma of the left thumb The XML misunderstanding We see:    The machine sees:

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 The clever (?) business man and his XML card John Nitwit 524 Moon base avenue Utopia … Is this the name of the business card or of the business card owner?

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 Intermediate conclusion We need for sure methods and techniques that allow: –people to express exactly what they mean, –people to understand exactly what is communicated to them, –machines to communicate information without any distortion. Since information technology caused information overload to become a problem, we also need methods and techniques that allow machines to understand exactly what is communicated to them.

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 9 Relevance to Healthcare ‘Nuances in the English language can be both challenging and amusing, however, when variants in language impact treatment, safety and billing, it is all challenge and no humor. Although English contains a reasonable degree of conformity, divergence in phrasing and meaning can compound comprehension problems and impact patient safety. These language "woes" can be minimized through the use of sophisticated healthcare IT systems...’ Schwend GT. The language of healthcare. Variance in the English language is harming patients and hospitals' bottom lines. Is healthcare IT the solution? Health Manag Technol. 2008 Feb;29(2):14, 16, 18

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 The Healthcare Information Technology ‘solution’ Use of ‘terminological systems’ –A terminological system relates concepts, of a particular domain, among themselves and provides their terms and possibly their definitions and codes. Based on CEN/ISO standards 10 N. F. de Keizer, A. Abu-Hanna, J. H. M. Zwetsloot-Schonk. Understanding Terminological Systems I: Terminology and Typology. Methods of Information in Medicine 2000;39:16-21.

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 Active ISO definitions for ‘concept’ a unit of thought (1985-2011, 8) - with close variants: –a unitary mental representation of a real or abstract thing; an atomic unit of thought (2011, 1) –an internal conception of some thing; general notion or idea of some thing (2003, 1) –a general notion or idea of something (2003, 1) –a unit of thought constituted by a unique set of necessary characteristics (2005-2010, 2) –a unit of thought constituted through abstraction on the basis of properties common to a set of objects (2002-2005, 2) a unit of knowledge created by a unique combination of characteristics (2000- 2011, 13) a unit of knowledge pertaining to an action, condition, idea, object, situation or to sets of relationships thereof, which can be designated and defined by words or symbols, or which can be detected by the senses (2009, 1) an abstract entity for determining category membership (1997, 1) 11 ISO Online Browsing Platform (beta). http://www.iso.org/obp/ui/#search. Accessed Feb 29, 2012.http://www.iso.org/obp/ui/#search

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 What meaning of ‘concept’ has ISO in mind here? materiality: –concept that individual or an aggregate of errors, omissions and misrepresentations could affect the greenhouse gas assertion (2.12) and could influence the intended users’ (2.24) decisions –Defined in: ISO 14064-1:2006, 2.28 12 ISO Online Browsing Platform (beta). http://www.iso.org/obp/ui/#search. Accessed Feb 29, 2012.http://www.iso.org/obp/ui/#search

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 SNOMED about diseases and concepts (until 2010) ‘Disorders are concepts in which there is an explicit or implicit pathological process causing a state of disease which tends to exist for a significant length of time under ordinary circumstances.’ And also: “Concepts are unique units of thought”. College of American Pathologists. SNOMED Clinical Terms® User Guide. January 2003 Release. Thus: Disorders are unique units of thoughts in which there is a pathological process …??? And thus: to eradicate all diseases in the world at once we simply should stop thinking ?

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 A correction long waited for Concept’ : ‘an ambiguous term. Depending on the context, it may refer to: a clinical idea to which a unique ConceptId has been assigned; the ConceptId itself, which is the key of the Concepts Table ([…] “concept code”); the real-world referent(s) of the ConceptId, that is, the class of entities in reality which the ConceptId represents ([…] “meaning” or “code meaning”)’ Jan 2010 SNOMED Technical Reference Manual 14

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 What do ‘concept’ and ‘conceptID’ denote? Merely pointing the ambiguity out is insufficient: –insufficient context for disambiguation in SNOMED documentation leaves doubt: clinical ideas are real-world entities themselves – some being such that they are about other real-world entities while others are about nothing at all SNOMED CT authors have not yet made it clear what sorts of real-world entities their concepts represent –relying on ‘meaning’ rather than ‘concept’ just pushes the problem forward. 15

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 The ambiguity of ‘meaning’ (1) SNOMED CT’s ‘concept’ definition suggests the meaning of a concept(Id) to correspond to Frege’s ‘Bedeutung’ (‘reference’, ‘extension’) of a term [1]. The User Guide says: ‘a “concept” is a clinical meaning identified by a unique numeric identifier (ConceptId) that never changes’ [16]. Here, the word ‘meaning’ corresponds rather to Frege’s ‘Sinn’ (‘sense’, ‘intension’) [1]. SNOMED-CT Editorial Guide: SNOMED is a ‘terminological resource’ which ‘consists of codes representing meanings expressed as terms, with interrelationships between the codes to provide enhanced representation of the meanings’ [2]. 16 [1] Frege G. Über Sinn und Bedeutung. Zeitschrift für Philosophie und philosophische Kritik. 1892;100:25-50. [2] International Health Terminology Standards Development Organisation. SNOMED CT® Editorial Guide - January 2011 International Release - (US English) 2011.

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 The ambiguity of ‘meaning’ (2) Leads to odd statements in SNOMED CT documentation [11]: –‘The meaning of a Concept does not change [emphasis added]’, immediately followed by the sentence : –‘If the Concept’s meaning changes because it is found to be ambiguous, redundant or otherwise incorrect, the Concept is made inactive [emphasis added]’ 17

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 Health IT’s meaning model: the semantic triangle term concept referent

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 What would a corresponding concept be ? term concept referent ‘Ludwig von Beethoven’ ?

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 What would a corresponding concept be ? term concepts referent ‘Beethoven’ the deaf composer that wrote nine symphonies that great German composer that became deaf …

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 Names versus descriptions (intensions) terms concepts referent ‘Beethoven's Symphony No. 3’ Beethoven’s symphony dedicated to Bonaparte the symphony played after the Munich Olympics massacre … ‘Beethoven's Opus 55’ ‘Eroica’

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 Empty extensions term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth …

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 Empty extensions term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth … some hold this term has meaning

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 Odd cases term concept referent ‘Beethoven's Symphony No. 10’ the one assembled by Barry Cooper from fragmentary sketches Beethoven’s hypothetical symphony …

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 Prehistoric ‘psychiatry’: drapetomania term concept referent ‘drapetomania’ disease which causes slaves to suffer from an unexplainable propensity to run away … painting by Eastman Johnson. A Ride for Liberty: The Fugitive Slaves. 1860.

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 Caught in framework ‘general concept’: –concept that corresponds to two or more objects that form a group by reason of common properties; –for example, the term “tower” is considered a general concept and the term “Eiffel Tower” is considered to be an individual concept Defined in: ISO 1942:2009, A.2.4  distinction between term and concept blurred 26

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 Further issues Similarities between numerically distinct entities (particulars) is only accounted for at the level of concepts. Lost distinction between terminology and ontology, not only in health IT: –  ‘ontology’ as a formal (= logical computer language based) representation of concept systems. 27

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 An alternative

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 Its implementation: Basic Formal Ontology term concept referent representational unit universal particular Concept-basedBasic Formal Ontology First order reality

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 30 A house keeping proposal

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 ‘Concept’ Definition: meaning of a term as agreed upon by a group of responsible persons –there can be agreed meanings for terms like “unicorn” which do not correspond to any unit of knowledge –‘concepts’ as common understandings of the meaning of a term distinguishes concepts from ideas in the minds of individual cognitive subjects. –the term refers to this meaning itself and not to any specification of this meaning in some natural or artificial language or in some formal model 31

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 Concept definition Definition: specification of a concept (i.e. of the agreed meaning of a term) by means of a descriptive statement or a formal expression which serves to differentiate it from other concepts –There may be more than one definition which captures the same agreed meaning. 32

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 Concept system Definition: collection of representations of concepts structured by means of representations of relations –a concept system is a collection of elements (called concept system nodes) which are related together via interconnections representing relations such as narrower_than and broader_than between the corresponding meanings. 33

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 Concept system node Definition: information element within the structure of a concept system that is a pointer linking one or several synonymous terms with a given concept definition and linked to other such information elements in the representation of relations between the corresponding concepts 34

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 Ontology Definition: a representational artifact, comprising a taxonomy as proper part, whose representational units are intended to designate some combination of types, classes, and certain relations between them 35

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 Realism-based ontology Definition: an ontology built out of terms which are intended to refer exclusively to types and which correspond to that part of the content of a scientific theory that is captured by its constituent general terms and their interrelations. 36

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 Consequences What we win: –clean separation between the two approaches, –identification of partial parallelism between the two approaches; What still lacks: –ways to use them together. 37

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 And we must use them together For instance in: OPMQoL: an Ontology for pain-related disablement, mental health and quality of life 1.study the terminology and ontology of pain as currently defined, 2.find ways to make individual data collections more useful for international research, 3.develop an ontology for integrating knowledge and data over all the known basic and clinical science domains concerning TMD and its relationship to complex disorders, and 4.expand this ontology to cover all pain-related disorders.

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 Acknowledgement The work described is funded in part by grant 1R01DE021917-01A1 from the National Institute of Dental and Craniofacial Research (NIDCR). The content of this presentation is solely the responsibility of the author and does not necessarily represent the official views of the NIDCR or the National Institutes of Health.

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 ‘meaning’ of values in data collections 1 ‘The patient with patient identifier ‘PtID4’ is stated to have had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ meaning terms used defined in concept-based terminologies

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 Not everything is as straightforward as anatomy The IASP definition for ‘pain’: –‘an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage’; what asserts: –a common phenomenology (‘unpleasant sensory and emotional experience’) to all instances of pain, –the recognition of three distinct subtypes of pain involving, respectively: 1.actual tissue damage, 2.what is called ‘potential tissue damage’, and 3.a description involving reference to tissue damage whether or not there is such damage.

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 Results of the ontological analysis Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32. http://www.referent-tracking.com/RTU/sendfile/?file=painTokyo1_27_2011.pdf http://www.referent-tracking.com/RTU/sendfile/?file=201201PainProject.ppt For ontological definitions of these types, see:

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 General framework of scientific research observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome

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 observation & measurement A crucial distinction: data and what they are about data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome First- Order Reality Representation is about???

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 Δ = outcome Data must be unambiguous and faithful to reality … ReferentsReferences organized in a data collection

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 … even when reality changes Are differences in data about the same entities in reality at different points in time due to: –changes in first-order reality ? –changes in our understanding of reality ? –inaccurate observations ? –registration mistakes ? Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.AMIA 2006 http://www.referent-tracking.com/RTU/sendfile/?file=CeustersAMIA2006FINAL.pdf

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 Methods to achieve faithfulness and data clarity SourcesData generationData organization Data collection sheets Instruction manuals Interpretation criteria Diagnostic criteria Assessment instruments Terminologies Data validation procedures Data dictionaries Ontologies If not used for data collection and organization, these sources can be used post hoc to document, and perhaps increase, the level of data clarity and faithfulness in and comparability of existing data collections.

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 An ontology to make data collections comparable Linking the variables of distinct data collections to a realism-based ontology.

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 Linking data collections using ontology and terminology

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 Work in progress: a framework for data collections Can we create a framework in which both terminologies and realism-based ontologies are formally related ? 50

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 data collection data item 1 1..* A data collection consists of at least 1 data item, each data item belonging to exactly 1 collection

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 data collection data dictionary uses 1..* 1 used-for data item 1 1..* 1 explained-in 1..* explains Data dictionaries provide information about data items and data collections

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 data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for data item 0..* terminology 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* Data dictionaries provide also information about terminologies and assessment instruments used for data generation, in addition to information about the collection’s structure

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 data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for data item 0..* terminology 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* Relation of Terminology component to Data component Terminology component Data component

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 term concept 1 broader narrower 1..* used in uses 0..* 1..* means expressed- by data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for Terminology component Data component data item 0..* terminology 1 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* Terminology links terms to ‘concepts’

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 term concept 1 broader narrower 1..* used in uses 0..* 1..* means expressed- by data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for Terminology component Data component data item 0..* terminology 1 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* Not ‘concepts’ are of interest, but entities in reality Ontology component entity

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 term concept 1 broader narrower 1..* used in uses 0..* 1..* means expressed- by data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for Terminology component Data component data item 0..* terminology 1 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* It is real entities that should be denoted in ontologies Ontology component entity ontology reference ontology 1..* 1 denotes 0..* denoted by 1 denotator

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 term concept 1 broader narrower 1..* used in uses 0..* 1..* means expressed- by data collection assessment instrument data dictionary uses used-in 1..* uses 1..* 1 used-for Terminology component Data component data item 0..* terminology 1 1 1..* uses 1..* used for 0..* 1 explained-in 1..* explains uses 1..* used for 0..* Application ontologies cover the domains of the sources Ontology component entity ontology reference ontology 1..* 1 denotes 0..* denoted by 1 denotator 1 used-for 1..* used for 1 uses application ontology data collection ontology assessment instrument ontology 1 uses

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 term concept 1 broader narrower 1..* used in uses 0..* 1..* means expressed- by data collection assessment instrument 0..1 expresses 0..* ontology data dictionary uses used-in 1 1..* uses 1..* 1 reference ontology 1..* used-for 1..* used for 1 uses bridging axiom used-for used for 0..* uses 1..* application ontology Terminology component Data component Ontology component data item representational artifact 1..* data collection ontology assessment instrument ontology 1 uses 0..* terminology 1..* uses 1 used-for entity 1 denotes 0..* denoted by 1 1 1..* uses 1..* used for 0..* 1 corresponds-to 1 explained-in 1..* explains uses 1..* used for 0..* denotator Bridging axioms link data to ontologies and terminologies

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 A candidate general framework 60

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 Current IAO version – IMHO – does not: –contain all necessary representational units, –define well enough what is present. 61

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 My first proposal (1) 62 TermDefinition Information Content Entity (ICE) an entity that is generically dependent on some artifact and stands in relation of aboutness to some portion of reality [4] Representational Artifact (RA) an ICE which is believed to represent a portion of reality external to the representation (modified from [5] ) Data Collectiona RA built out of data items about a portion of reality, each data item believed to denote exactly one particular Terminologya RA consisting of terms (modified from [5]) Composite Representationa RA built out of constituent sub-representations as its parts (modified from [5]) Representational Unit (RU) a RA which according to the structural conventions it is designed, is not built out of any other RAs Denotatora RU which denotes directly an entity in its entirety without providing a description [6] Term a RU which is a general term in some natural language used to refer to portions of reality in some specific domain (modified from [5])

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 My first proposal (2) 63 Ontologya RA comprising a taxonomy as proper part, whose RUs are intended to designate some combination of universals, defined classes, and certain relations between them [3] Realism-based Ontologyan ontology built out of RUs which are intended to refer exclusively to universals, i.e. intended to mimic the structure of reality, and which correspond to that part of the content of a scientific theory that is captured by its constituent general terms and their interrelations [3] Reference Ontologyan ontology intended to provide an informationally complete representation of a domain Application Ontologyan ontology representing the portion of reality which is relevant for some purpose in some community Assessment Instrument Ontology an application ontology describing the portion of reality covered by an assessment instrument Dataset Ontologyan application ontology describing the portion of reality covered in a data collection

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 My first proposal (3) 64 Data Itema RA that is intended to be a truthful statement about something (modulo, e.g., measurement precision or other systematic errors) and is constructed/acquired by a method which reliably tends to produce (approximately) truthful statements (modified from [4]) Data Setan aggregate of other data items of the same type that have something in common [4] Directive Information Entityan ICE whose concretizations indicate to their bearer how to realize them in a process [4] Conditional Specificationa directive information entity that specifies what should happen if a trigger condition is fulfilled [4] Rulean executable which guides, defines, restricts actions [4] Bridging Axiom a rule specifying how a RA should be interpreted in terms of an application ontology Data Format Specificationthe information content borne by the document published defining the specification [4] Plan Specificationa directive information entity that when concretized is realized in a process in which the bearer tries to achieve the objectives, in part by taking the actions specified [4] Documenta collection of ICE intended to be understood together as a whole [4] Assessment Instrumenta document containing directive information entities designed to compile data collections reliably, validly and reproducibly Data Dictionarya document containing directive information entities designed to facilitate the interpretation of that what data items in a data collection are about

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 References used [3]Klein GO, Smith B. Concept Systems and Ontologies: Recommendations for Basic Terminology. Japanese translation in Journal of the Japanese Society for Artifical Intelligence, 25 (3), 2010, 317-325. 2010;25:433-41. [4]Information Artifact Ontology. 2012 [cited 2012 January 24]; Available from: http://code.google.com/p/information-artifact-ontology/. [5]Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. KR-MED 2006, Biomedical Ontology in Action. Baltimore MD, USA 2006. [6]Ceusters W, Manzoor S. How to track Absolutely Everything? In: Obrst L, Janssen T, Ceusters W, editors. Ontologies and Semantic Technologies for the Intelligence Community Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press; 2010. p. 13-36. 65

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 Major weakness The BFO community in general and the IAO community in specific has left thus far –too many issues in the open –too many terms insufficiently defined; a first review by Alan Ruttenberg revealed that I understood some definitions differently than how they were supposed to be understood, some notes in the IAO mystify more than clarify: –“2009-03-16: data item deliberately ambiguous: we merged data set and datum to be one entity, not knowing how to define singular versus plural. So data item is more general than datum.” 66

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 We need still to get the basic elements right ‘information’ ‘denotation’ ‘representation’ ‘being about’ … problem: whatever choice made, there will be a clash between technical and general linguistic use of terms. 67

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 ‘Information’ Are (or convey?) the following sentences information: –‘Brussels is the capital of Belgium’ –‘Brussels is the capital of France’ –‘James Bond drives a Porsche’ For the IAO: –an information content entity (ICE) does not need to be about anything, –can be about something completely different than what it is about at face value. 68

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 Representation ‘A REPRESENTATION is for example an idea, image, record, or description which refers to (is of or about), or is intended to refer to, some entity or entities external to the representation. Note that a representation (e.g. a description such as ‘the cat over there on the mat’) can be of or about a given entity even though it leaves out many aspects of its target.’ 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.

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 POR must be outside the representation Are the following sentences representations: –‘This sentence is a representation of itself’, –‘The second word in this sentence has 6 letters’, –‘Werner Ceusters wrote this Powerpoint presentation’? 70

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 For a representation to exist some entity must: –have brought R into existence, –have postulated the existence of POR, –have had the intention for R to be about POR, –have believed at the time of bringing R into existence that it is indeed about POR. I believe that this must be reflected in that what is an ICE  use of ‘artifact’ 71

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 Bringing R into existence At 9AM, Werner promises Anne to let her know whether his 3PM presentation was successful, by afterwards quickly coming home to leave the message accordingly before taking off to another meeting from which he will not return before Anne comes home. Qua form of the message, they discuss the options: –writing on a piece of paper: ‘good’ or ‘bad’, –leaving the kitchen door in the position it is when he comes home in case the presentation was successful, or changing it otherwise. 72

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 The ‘good’ / ‘bad’ on paper case What is the representation? –the paper, the ink pattern on the paper ? Distinction between: –bearer, –information content entity (ICE) The ICE is concretized in the bearer. When is the representation created? 73

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 What about the kitchen door ? At least, kitchen doors will never be representational units in a realism-based ontology 74

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 A typology of representational elements Referring representational unit (RRU): an RU which is both intended to denote something and indeed succeeds in doing so. Non-referring representational unit (NRU): an RU which, for whatever reason, fails to denote something. Unrecognized non-referring representational unit (UNRU): an NRU which, although non-referring, is intended and believed to have a referent. Recognized non-referring representational unit (RNRU): an NRU which was once intended and believed to have a referent, but which, as a result of advances in knowledge, is no longer believed to do so. Representational unit component (RUC): a component of a representational artifact that is not intended by the artifact’s authors to refer in isolation. Ceusters W, Smith B. Foundations for a realist ontology of mental disease. Journal of Biomedical Semantics, 2010, 1:10 (9 December 2010).

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 for this presentation, since there is not yet much to conclude about Conclusion 76

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 A distinction I distinguish two topics: –first, the description of possible languages or grammars as abstract semantic systems whereby symbols are associated with aspects of the world; and, second, –the description of the psychological and sociological facts whereby a particular one of these abstract semantic systems is the one used by a person or population. –Only confusion comes of mixing these two topics. 77 Lewis, D., 1970, “General Semantics,” Synthese22: 18–67, p19.

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 Philosophers about ‘meaning’ Semantic theories Propositional semantic theories –Possible worlds semantics –Russellian propositions –Fregean propositions Non-propositional theories –Davidsonian semantics –Chomskyan internalist semantics Foundational theories of meaning Mentalist theories –The Gricean program –Meaning, belief, and convention –Mental representation-based theories Non-mentalist theories –Causal origin –Truth-maximization and the principle of charity –Regularities in use –Social norms 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 Final questions Are we, while building realism-based ontologies, –ignoring meaning theory at all, and if so, is that justifiable, –or too much focused on / biased towards semantic theories? 79