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Presentation on theme: "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."— Presentation transcript:

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 Theme: Ontology for Ontologies, theory and applications Inaugural Oration: The quest for semantic interoperability May 17, 2010; 16h30-19h00 Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels Room D2.01 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 Buffalo NYC Chicago

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

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 Center of Excellence in Bioinformatics & Life Sciences Buffalo, NY

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

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 ? Short personal history

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 ? Short personal history ?

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 A trajectory of mixes and mingles … 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

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 … provides the context for this lecture series (1) May 17: the quest for semantic interoperability –what is it ? –what are the building blocks ? –why do only few systems exhibit it ? –Take home message: good ontologies are badly needed Informatics Knowledge Representation Electronic Health Records PhilosophyOntology

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 … provides the context for this lecture series (2) May 18: the need for realism-based ontology development –What ontology should be philosophical realism, applied to … … ‘knowledge representation’ –Generic/specific distinction relation with Referent Tracking –Target audience: ontology developers and evaluators philosophers who want a real job technology scouts –Take home message: good ontology = realism-based ontology Referent Tracking PhilosophyOntology Realism-Based Ontology

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 … provides the context for this lecture series (3) May 19: ontologies in healthcare and the vision of personalized medicine –An ontologist’s view on data and information models –Open Biomedical Ontologies Foundry –Example ontologies for eHealth Biology Translational Research Pharmacology PharmacogenomicsMedicine Electronic Health Records Referent Tracking Realism-Based Ontology

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 … provides the context for this lecture series (4) May 20: ontologies and Natural Language Understanding Target audience: –computational linguists –semantic engineers Linguistics Computational Linguistics Medical Natural Language Understanding Informatics Medicine Electronic Health Records Realism-Based Ontology

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 … provides the context for this lecture series (5) May 21: Referent Tracking: why Big Brother was just a little baby. –theory of Referent Tracking: give a unique identifier to everything –implementation of RT systems –application in situational awareness (in the broadest sense) –Target audience: everybody who wants to survive after 2012 Defense & Intelligence Performing Arts Referent Tracking Realism-Based Ontology

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 Semantic Interoperability

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 Interoperability of Information Systems The capacity of distinct information systems to exchange ‘ stuff ’ From ‘Wargames’

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 Gradations in interoperability Level 0: no interoperability at all Level 1: technical and syntactical interoperability (no semantic interoperability) Level 2: two orthogonal levels of partial semantic interoperability –Level 2a: unidirectional semantic interoperability –Level 2b: bidirectional semantic interoperability of meaningful fragments Level 3: full semantic interoperability, sharable context, seamless co-operability Semantic Interoperability for Better Health and Safer Healthcare. Semantic HEALTH Report. January 2009

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 One often used definition Semantic Interoperability (SI) = the ability of two or more computer systems to exchange information in such a way that the meaning of that information can be automatically interpreted by the receiving system accurately enough to produce useful results to the end users of both systems.

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 ‘Full interoperability’ ‘Neither language nor technological differences prevent the system to seamlessly integrate the received information into the local record and provide a complete picture of someone’s health as if it would have been collected locally.’ ‘Further, the anonymized data feeds directly into the tools of public health authorities and researchers.’ Stroetmann et.al. Semantic Interoperability for Better Health and Safer Healthcare. SemabticHEALTH report. Jan 2009

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 this practically means … Healthcare Finance Intelligence and Command & Control Digital collections and IP rights Enterprise & supply chain management …

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 Biggest SI endeavor: the Semantic Web

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 The standard web: end users are humans

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 The Semantic Web: end-users are maximally assisted by agents

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 Where is a web … is usually a spider

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 core issue: Semantic Interoperability (SI) = the ability of two or more computer systems to exchange information in such a way that the meaning of that information can be automatically interpreted by the receiving system accurately enough to produce useful results to the end users of both systems. meaning

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 And meaning is, of course, the problem ‘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).

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 Goal of the Semantic Web to make it possible for software to find the data it needs on the Web, understand it, cross-reference it and apply it to a particular task. “I should be able to tell my Web-enabled handheld device to schedule an appointment with a dentist within 20 miles of home and let the computer do the rest.”

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 “I should be able to tell my Web-enabled handheld device to schedule an appointment with a dentist within 20 miles of home and let the computer do the rest.” So the SW must understand natural language ? So the SW must know when the requester is free ? So the SW must understand that it is to take care of the requester’s teeth, and not to have a nice diner date ? So the SW can then deduce what the actual length of “20 miles” is for this particular person ? So the SW must understand where the requester lives ?      If it were just that simple

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 Pray your computer isn’t Irish X: “Hallo stranger, you appear to be travelling?” Y: “Yes, I always travel when on a journey.” X: “And pray, what might your name be?” Y: “It might be Sam Patch, but it isn't.” X: “Have you been long in these parts?” Y: “Never longer than at present—5 feet 9.” X: “Do you get anything new?” Y: “Yes, I bought a new whetstone this morning.” Copyright © 1996 Electronic Historical Publications

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 The linguistic perspective (1) characters lexemeswordssyntax semantics word categories pragmatics discourse morphology phrases sentences prose

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 The linguistic perspective (2) Words: –‘in’, ‘hepatitis’, ‘the’, ‘virus’, ‘sit’, ‘bank’, ‘river’, ‘money’ We combine them in phrases and sentences: –‘hepatitis virus’ ‘virus hepatitis’, –‘money in the bank’‘bank in the river’ We combine sentences: –‘First I removed the skin from the fish. Then I fried it. It was delicious.’ We know what (not) to use under which circumstances: –‘girl’ – ‘chick’,‘man’ – ‘guy’ – ‘dude’, …

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 Building the Semantic Web requires this too

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 But it seems that only dummies are involved there must be a lot of dummies, or don’t they still get it? a lot does seem to mean nothing at all

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 The clever (?) business man and his XML card John Nitwit 524 Moon base avenue Utopia …

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 Is anything gained this way? Eric Miller. Weaving Meaning: The Semantic Web

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 Are mails like this one surprising? At 10:13 PM 3/22/2010, you wrote: Dear Prof Smith, just a quick to express my sincerest gratitude - the learning materials you made available are being of enormous value to me. After a PhD in the Semantic Web area at [a well known knowledge management institute], I came out so disgusted with the general lack of scientific & philosophical grounding in the community around me, that I felt I totally lost sight of my research path. But your systematic and thorough presentation of the field is helping me see where I stand, without all the usual technical buzzwords and marketing pitches. At the same time, this gives me hope of finding more solid grounds for my future research.

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 heart of the evil …

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 What it was …

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 became

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 What is (an) Ontology ? Without buzzwords and marketing pitches but with adequate philosophical thinking

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 “What is … ?” –questions are problematic How would you answer the following questions: –what is a human being ? –what is JFK ? –what is yellow ? –what is a unicorn ? –what is a drug ?

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 What do the following juxtapositions reveal? what is a human being? what is JFK? what is yellow? what is a unicorn? what is a drug? what does ‘human being’ mean? what does ‘JFK’ mean? what does ‘yellow’ mean? what does ‘unicorn’ mean? what does ‘drug’ mean?

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 What do the following juxtapositions reveal? what is a human being? what is JFK? what is yellow? what is a unicorn? what is a drug? what does ‘human being’ mean? what does ‘JFK’ mean? what does ‘yellow’ mean? what does ‘unicorn’ mean? what does ‘drug’ mean? OntologyTerminology

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 The Ontology-Terminology divide Ontology is about what things are. Terminology is about how to name things, without caring about whether what is named exists. Sadly, this distinction is by many people who call themselves ‘ontologists’ or build ‘ontologies’ either not understood at all, or applied in the wrong way.

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 Terminological versus Ontological approach The terminologist defines: –‘a clinical drug is a pharmaceutical product given to (or taken by) a patient with a therapeutic or diagnostic intent’. (RxNorm) The (good, real) ontologist thinks: –Does ‘given’ includes ‘prescribed’? –Is manufactured with the intent to … not sufficient? Are newly marketed products – available in the pharmacy, but not yet prescribed – not clinical drugs? Are products stolen from a pharmacy not clinical drugs? What about such products taken by persons that are not patients? –e.g. children mistaking tablets for candies.

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 dichotomy is also present in simple words Carl Austin Weiss, MD (Dec 6, 1906 – Sept 8, 1935) Huey Pierce Long, Jr. (Aug 30, Sept 10, 1935) Solving crimes through Referent Tracking

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 A double mystery (It is argued that) On September 9th, 1935, Carl Austin Weiss shot Senator Huey Long in the Louisiana State Capitol with a.35 calibre pistol. Long died from this wound thirty hours later on September 10th. Weiss, on the other hand, received between thirty-two and sixty.44 and.45 calibre hollow point bullets from Long's agitated bodyguards and died immediately. Sorensen, R., 1985, "Self-Deception and Scattered Events", Mind, 94: Questions: –Did Weiss kill Senator Long ? –If so, when did he kill him ?

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 The events on a time line time Senator Long’s living Weiss’ shooting of Long Carl Weiss’ living Bodyguards’shooting of Weiss Weiss’ path. body reactions Long’s pathological body reactions

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 When did the killing happen ? time Senator Long’s living Weiss’ shooting of Long Carl Weiss’ living Bodyguards’shooting of Weiss Weiss’s path. body reactions Long’s pathological body reactions t1?t1? t2?t2? If at t 1 : Long was not dead after he was killed If at t 2 : Long was killed by a dead person

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 What this demonstrates What things are and how things are named, are two different issues, (Natural) language does not fit nicely with reality, –formed at a time when insight in reality was crippled, –did not evolve with our insight, Human brains have the capacity not to be bothered too much by the unfaithfulness of natural language.

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 Ambiguous phrasings warning on plastic bag

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 Hotel semantics in Miami hotel lobbyin A’dam hotel elevator

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 Good philosophers lack this capacity

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 Computers lack this ability too, but that is a problem Knowledge representation and semantic interoperability are for machines, not humans; Computer languages and knowledge representations must at least be unambiguous, and preferably also faithful to (our best understanding of) reality. Unfortunately, the majority of them don’t precisely because of the confusion between terminology and ontology.

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 The terminology of ‘ontology’: Google  ‘define: ontology’: the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

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 One term, many definitions This raises some (philosophical?) questions: 1.Is it possible for a term to have so many meanings? 2.Can the authors of these definitions all be right at the same time? 3.Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ?

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 Clearly: yes ! This phenomenon is called: and is usually explained in terms of the semantic or semiotic or meaning triangle. Homonymy Q1: Is it possible for a term to have so many meanings?

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 Standard Semiotic/Semantic Triangle

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 Standard Semiotic/Semantic Triangle Useful, but nevertheless wrong !

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 Useful to build multi-lingual dictionaries Concept ‘cat’ cat chat kat Katze …

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 Problem: several interpretations of the Semiotic/Semantic triangle Sign: Language/ Term/ Symbol Referent: Reality/ Object Reference: Concept / Sense / Model / View / Partition

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 Aristotle’s triadic meaning model semeia gramma/ phoné pragma pathema Words spoken are signs or symbols (symbola) of affections or impressions (pathemata) of the soul (psyche); written words (graphomena) are the signs of words spoken (phoné). As writing (grammatta), so also is speech not the same for all races of men. But the mental affections themselves, of which these words are primarily signs (semeia), are the same for the whole of mankind, as are also the objects (pragmata) of which those affections are representations or likenesses, images, copies (homoiomata). Aristotle, 'On Interpretation', 1.16.a.4-9, Translated by Cooke & Tredennick, Loeb Classical Library, William Heinemann, London, UK, 1938.

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 Richards’ semantic triangle Reference (“concept”): “indicates the realm of memory where recollections of past experiences and contexts occur”. Hence: as with Aristotle, the reference is “mind- related”: thought. But: not “the same for all”, rather individual mind-related symbolreferent reference understandingmy your understanding

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 Don’t confuse with homonymy ! “mole” mole (animal) R1 mole (unit) R2 mole (skin lesion) R3

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 Different thoughts Homonymy “ mole ” mole “ animal ” R1 mole “ unit ” R2 mole “ skin lesion ” R3 symbol referent understanding One concept of x understanding of y

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 And by the way, synonymy... the Aristotelian viewRichards’ view “perspiration” “sweat” “perspiration”

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 Frege’s view “sense” is an objective feature of how words are used and not a thought or concept in somebody’s head 2 names with the same referent can have different senses –morning star –evening star 2 names with the same sense have the same referent (synonyms) a name with a sense does not need to have a referent (“Beethoven’s 10 th symphony”) referent sense name

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 Homonymous use of the term ‘ontology’ the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

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 Q2: Can the authors of these definitions all be right at the same time? Yes, if we are dealing with a case of homonymy. But in that case, they are all talking about different distinct things.

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 Q3: Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ? study representation vocabulary specification information model data model ‘that’ thing is an is a ? (hint on next slide)

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 Remember the “what is yellow?”-question Answers could have been: –a color –a banana Thus: –can something which is a color be a banana ? –can something which is a banana be a color ?

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 Q3: Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ? Not for all ! Only for some

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 Homonymous use of the term ‘ontology’: at least one clear cut distinction the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

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 ‘Ontology’ as the study of what exists Key questions: –What exists ? –How do things that exist relate to each other ? Some hypotheses: –An external reality, time, space –Ideas, concepts –Particulars, universals, objects, processes –God Ontologists from distinct ‘schools’ differ in opinion about the existence of some of the above: –Realism, nominalism, conceptualism, monism, …

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 An ontology as a representation Terms  WordNet, MedDRA, RxNORM Concepts  the majority of ‘ontologies’ But … overwhelming lack of clarity about what ‘concepts’ are: meaning shared in common by synonymous terms ? idea shared in common in the minds of those who use these terms ? unit of knowledge describing meanings ? feature or property or characteristic shared in common by entities in the world ? Universals  Realism-based ontology Key question: of what ?

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 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort Most ontologies are ‘concept’-based 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

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 Concepts in ISO ? A unit of thought constituted through abstraction on the basis of properties common to a set of objects. (ISO 1087:1990) –Object: anything perceivable or conceivable. Objects may also be material (e.g. an engine, a sheet of paper, a diamond), immaterial (e.g. a conversion ratio, a project plan) or imagined (e.g. a unicorn). [Adapted from ISO :2000, 3.1.1] A unit of knowledge created by a unique combination of characteristics. [ISO :2000, 3.2.1] –characteristic: Abstraction of a property of an object or of a set of objects. Characteristics are used for describing concepts. [ISO :2000, 3.2.4] What knowledge is there to have about unicorns ?

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 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: Most terminologies are ‘concept’-based

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 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: This view does not presuppose cognition at all Most terminologies are ‘concept’-based

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 Therefore: a multi-disciplinary approach to ontology In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; Our ‘realist’ view within the Ontology Research Group combines the two: –We use realism, a specific theory of ontology, as the basis for building high quality ontologies, using reality as benchmark. In mainstream computer science and biomedical informatics: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain;

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 Realism-based Ontology Accepts the existence of: –a real world outside mind and language, –a structure in that world prior to mind and language (universals / particulars). Rejects ontology as a matter of agreement on ‘conceptualizations’. Uses reality as a benchmark for testing the quality of ontologies as artifacts by building appropriate logics with referential semantics (rather than model-theoretic).

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 A realism-based ontology is … a representation of some pre-existing domain of reality which: –(1) reflects the properties of the entities within its domain in such a way that there obtains a systematic correlation between reality and the representation itself, –(2) is intelligible to a domain expert, –(3) is formalized in a way that allows it to support automatic information processing.

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 Compare with Alberti’s grid reality representation Ontological theory

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 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

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 Rise and fall of the concept-based approach

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 No serious scholar should work with ‘concepts’

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 Slow penetration of the idea …

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 More serious scholars become convinced … what is a concept description a description of?

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 Eugen Wüster 1935 Professor of Woodworking Machinery in the Vienna Agricultural College Terminology-hobbyist founder of ISO-TC 37: terminology standards

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 concepts are inside people’s brains –a concept is a mental surrogate of a plurality of objects grouped together on the basis of perceived similarities –what makes those objects similar is itself a concept object = def. anything to which human thought is or can be directed, whether material or immaterial, real or purely imagined ISO: ‘In the course of producing a terminology, philosophical discussions on whether an object actually exists in reality … are to be avoided’. Eugen Wüster’s psychological view of concepts

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 Concept-based approaches are top-down FIRST concepts (meanings, words, terms) THEN (if you’re lucky) real-world phenomena Reasons: – Wüsterianism and the ISO terminology standards – needs of programmers (and of third-party payers) – hold-overs from the era of electronic dictionaries Smith B., Ceusters W, Temmerman R. Wüsteria. In: Engelbrecht R. et al. (eds.) Medical Informatics Europe, IOS Press, Amsterdam, 2005;:

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 Typical reasoning patterns for Wüsterians If domain experts use some term –then, there must be a concept, whether or not there is some referent. If observations reveal the existence of ‘objects’ which are of a similar kind, –then, even if we don’t know yet what that kind is, –there must be an associated concept.

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 Observations and similarities

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 Observations and similarities Are these pictures of concepts or of horses ? Is this a sensible question: ‘What concepts have tails and do …?’

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 Observations and similarities Are these pictures of concepts? Are these pictures of anything at all? If concepts are in brains, that must be awfully big brains!

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 Concepts = confusions ! Use/mention confusions: –Brussels is a nice city and has eight letters.  Brussels is a nice city and Brussels’ name is ‘Brussels’ and ‘Brussels’ has eight letters. Kantian confusions: –what exists is what we believe that exists –horses exists because we have the concept of horse and we see in reality things that fit that concept.

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 And confusion is thus everywhere in terminologies, classifications and ‘ontologies’ SNOMED: –‘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”. –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 ?

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 SNOMED International (1995, V3.1) TTopography12,385 MMorphology 4,991 FFunction16,352 LLiving Organisms24,265 CDrugs & Biological Products14,075 APhysical Agents, Forces and Activities 1,355 DDisease/ Diagnosis28,623 PProcedures27,033 SSocial Context 433 JOccupations 1,886 GGeneral Modifiers 1,176 TOTAL RECORDS 132,641 ?

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 Diagnosis versus disease The disease is hereThe diagnosis is here

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 Border’s classification (of medicine?) Medicine –Mental health –Internal medicine Endocrinology –Oversized endocrinology Gastro-enterology... –Pediatrics –... –Oversized medicine Refer to the size of the books that do not fit on a normal Border’s Bookshop shelf

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 MeSH: Geographic Locations Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + mereological mess mixture of geographic entities with socio- political entities mixture of space and time

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 MeSH: Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z ] + Austria-Hungary [Z ] Commonwealth of Independent States [Z ] + Czechoslovakia [Z ] + European Union [Z ] Germany [Z ] + Korea [Z ] Middle East [Z ] + New Guinea [Z ] Ottoman Empire [Z ] Prussia [Z ] Russia (Pre-1917) [Z ] USSR [Z ] + Yugoslavia [Z ] +

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 Diabetes Mellitus in MeSH 2008 ? Different set of more specific terms when different path from the top is taken.

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 MeSH: some paths from top to Wolfram Syndrome Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases

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 What would it mean if used in the context of a patient ? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary has Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus ??? … has

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 Description logics is no guarantee to get parthood right SNOMED-RT (2000) SNOMED-CT (2003)

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 Mistakes due to inappropriate lexical mapping ?

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 Find the problem concept terms

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

109 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 CT (July 2007): “fractured nasal bones”

110 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-CT: abundance of false synonymy nose bones fracture

111 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 Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

112 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 patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

113 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 cycles in hierarchical relationships UMLS: Metathesaurus: merging terminologies

114 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

115 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 Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages by humans. It is NOT the right device –to explain why reality is what it is, how it is organised, etc., (although it is needed to allow communication), –to reason about reality, –to make machines understand what is real, –to integrate across different views, languages, conceptualisations,...

116 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 Why not ? Because there is no valid benchmark !

117 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 Why not ? Conceptualism does not take care of the structure of reality. Concepts not necessarily correspond to something that (will) exist(ed) –Sorcerer, unicorn, leprechaun,... Definitions set the conditions under which terms may be used, and may not be abused as conditions an entity must satisfy to be what it is.  Kantian constructivism Language can make strings of words look as if it were terms –“Middle lobe of left lung”

118 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 biggest problem: a confusion between “terminology” and “ontology” The conditions to be agreed upon when to use a certain term to denote an entity, are often different than the conditions which make an entity what it is. –Trees would still be different from rabbits if there were no humans to agree on how these things should be called. “ontos” means “being”. The link with reality tends to be forgotten: one concentrates on the models instead of on the reality.

119 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 to do about it ? (1) Research: –Revision of the appropriatness of concept-based terminology for specific purposes; –Relationship between models and that part of reality that the models want to represent; –Adequacy of current tools and languages for representation; –Boundaries between terminology and ontology and the place of each in semantic interoperability.

120 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 to do about it ? (2) Training and awareness –Make people more critical wrt terminology and ontology promisses What is needed must be based on needs, not on the popularity of a new paradigm But in a system, it’s not just your own needs, it is each component’s needs ! –Towards “an ontology of ontologies” First description Then quality criteria

121 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 based on Unqualified Realism Accepts the existence of –a real world outside mind and language –a structure in that world prior to mind and language (universals / particulars) Rejects nominalism, conceptualism, ontology as a matter of agreement on ‘conceptualizations’ Uses reality as a benchmark for testing the quality of ontologies as artifacts by building appropriate logics with referential semantics (rather than model-theoretic)

122 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 How that works ? Come and see tomorrow

123 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 get us tickets for tomorrow? Sure, for the train out of here. Boo, this was awful!!


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