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2 The First Industrial- Strength Philosophy

3 IFOMIS Institute for Formal Ontology and Medical Information Science

4 Medicine needs to find a way to enable the huge amounts of data resulting from formal trials and from informal clinical practice to be (f)used together

5 The problem Different communities of medical researchers use different and often incompatible category systems in expressing the results of their work

6 Example: Medical Nomenclature MeSH (Medical Subject Headings): blood is a tissue SNoMed (Systematized Nomenclature of Medicine): blood is a fluid

7 The solution “ONTOLOGY” Remover “Ontology Impedance” But what does “ontology” mean?

8 Two alternative readings Ontologies are oriented around terms or concepts = currently popular IT conception Ontologies are oriented around the entities in reality = traditional philosophical conception, embraced also by IFOMIS

9 Ontology as a branch of philosophy seeks to establish the science of the kinds and structures of objects, properties, events, processes and relations in every domain of reality

10 Ontology a kind of generalized chemistry or zoology (Aristotle’s ontology grew out of biological classification)

11 Aristotle world’s first ontologist

12 World‘s first ontology ( from Porphyry’s Commentary on Aristotle’s Categories)

13 Linnaean Ontology

14 Medical Diagnostic Ontology

15 Ontology is distinguished from the special sciences it seeks to study all of the various types of entities existing at all levels of granularity

16 and to establish how they hang together to form a single whole (‘reality’ or ‘being’)

17 Sources for ontological theorizing: the study of ancient texts thought experiments (we are philosophers, after all) the development of formal theories the results of natural science now also: working with computers

18 The existence of computers and of large databases allows us to express old philosophical problems in a new light

19 Example: The Gene Ontology (GO) hormone ; GO: %digestive hormone ; GO: %peptide hormone ; GO: %adrenocorticotropin ; GO: %glycopeptide hormone ; GO: %follicle-stimulating hormone ; GO: % = subsumption (lower term is_a higher term)

20 as tree hormone digestive hormone peptide hormone adrenocorticotropin glycopeptide hormone follicle-stimulating hormone

21 GO is very useful for purposes of standardization in the reporting of genetic information but it is not much more than a telephone directory of standardized designations organized into hierarchies

22 GO deals with such basic ontological notions very haphazardly GO’s three main term-hierarchies are: component, function and process But GO confuses functions with structures, and also with executions of functions and has no clear account of the relation between functions and processes

23 Moreover, GO can in practice be used only by trained biologists whether a GO-term stands in the subsumption relationship

24 A still more important problem There exist multiple databases: GDB Genome Database of Human Genome Project GenBank National Center for Biotechnology Information, Washington DC etc.

25 What is a gene? GDB: a gene is a DNA fragment that can be transcribed and translated into a protein GenBank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype GO uses ‘gene’ in its term hierarchy, but it does not tell us which of these definitions is correct

26 How resolve such incompatibilities? The Semantic Web Initiative (Tim Berners-Lee, the inventor of the internet): enforce terminological compatibility via standardized term hierarchies, with standardized definitions of terms applied as meta-tags to websites

27 The Semantic Web The Web is a vast edifice of heterogeneous data sources Needs the ability to query and integrate across different conceptual systems

28 Metadata: the new Silver Bullet We agree on a metadata standard for washing machines as concerns: size, capacity, energy consumption, water consumption, price We create machine-readable databases of our inventories and put them on the net A consumer can then query multiple sites simultaneously and thereby search the Internet for highly specific, reliable, context-sensitive results

29 Cary Doctorow: A world of exhaustive, reliable metadata would be a utopia.

30 Problem 1: People lie Meta-utopia is a world of reliable metadata. But poisoning the well can confer benefits to the poisoners Metadata exists in a competitive world. Some people are crooks. Some people are cranks.

31 Problem 2: People are lazy Half the pages on Geocities are called “Please title this page”

32 Problem 3: People are stupid The vast majority of the Internet's users (even those who are native speakers of English) cannot spell or punctuate Will internet users suddenly and en masse learn to accurately categorize their information according to whatever DL- hierarchy they're supposed to be using?

33 Problem 4: Metrics influence results raw MHz scores privilege Intel's CISC chips over Motorola's RISC chips. Every player in a metadata standards body will want to emphasize their high- scoring axes

34 Problem 5: Multiple descriptions We impart information He chatters They gossip Requiring everyone to use the same vocabulary to describe their material denudes the cognitive landscape, enforces homogeneity in ideas.

35 Problem 6: Ontology Impedance = semantic mismatch between ontologies being merged This problem recognized in Semantic Web literature: karlsruhe.de/About/Deliverables/ontoweb-del- 7.6-swws1.pdf

36 Solution 1: treat it as (inevitable) ‘impedance’ and learn to find ways to cope with the disturbance which it brings Suggested here: out/Deliverables/ontoweb-del-7.6-swws1.pdf

37 Solution 2: resolve the impedance problem on a case-by-case basis Suppose two databases are put on the web. Someone notices that "where" in the friends table and "zip" in a places table mean the same thing.

38 Both solutions fail 1.treating mismatches as ‘impedance’ inappropriate in an area like medicine and ignores the problem of error propagation 2. resolving impedance on a case-by- case basis defeats the very purpose of the Semantic Web

39 Problem 5: Multiple descriptions Requiring everyone to use the same vocabulary to describe their material not always practicable especially in the medical domain

40 Clinicians often do not use category systems at all – they use unstructured text from which useable data has to be extracted in a further step Reasons for this: every case is different, much patient data is context-dependent

41 Proposed IFOMIS solution distinguish two separate tasks: - the task of developing computer applications capable of running in real time -the task of developing an expressively rich framework of a sort which will allow us to resolve incompatibilities between definitions

42 different terminology systems

43 need not interconnect at all for example they may relate to entities of different granularity

44 we cannot make incompatible terminology-systems interconnect just by looking at concepts, or knowledge or language

45 we cannot make incompatible terminology-systems interconnect or by staring at the terminology systems themselves

46 to decide which of a plurality of competing definitions to accept we need some tertium quid

47 we need, in other words, to take the world itself into account

48 BFO = basic formal ontology

49 BFO ontology is defined not as the ‘standardization’ or ‘specification’ of conceptualizations (not as a branch of knowledge or concept engineering) but as an inventory of the entities existing in reality

50 The BFO framework will solve the problem of ontological impedance and provide tools for quality-control on the output of computer applications

51 BFO not a computer application but a Reference Ontology (something like old-fashioned metaphysics)

52 Reference Ontology a theory of a domain of entities in the world based on realizing the goals of maximal expressiveness and adequacy to reality sacrificing computational tractability for the sake of representational adequacy

53 Reference Ontology a theory of the tertium quid – called reality – needed to hand-callibrate database/terminology systems

54 Methodology Get ontology right first (realism; descriptive adequacy; rather powerful logic); solve tractability problems later

55 A reference ontology is a theory of reality But how is this possible? How can we get beyond our concepts?

56 Answer: draw on 2 millennia of philosophical research pertaining to realism, scepticism, error, theory change, and the language/concept/world relation pertaining to the structure of reality itself at different levels of granularity APPLY THE RESULTS TO THE DOMAIN OF MEDICAL REALITY

57 try to find ways to look at the same objects at different levels of granularity:

58 and also: look not at concepts, representations, of a passive observer but rather at agents (clinicians) acting in the world taking account of the tacit knowledge of reality which the domain experts possess GO useable only by biologists, because only they know how given terms function in given contexts

59 The Reference Ontology Community IFOMIS (Leipzig) Laboratories for Applied Ontology (Trento/Rome, Turin) Foundational Ontology Project (Leeds) Ontology Works (Baltimore) Ontek Corporation (Buffalo/Leeds) Language and Computing (L&C) (Belgium/Philadelphia)

60 Domains of Current Work IFOMIS Leipzig: Medicine, Bioinformatics Laboratories for Applied Ontology Trento/Rome: Ontology of Cognition/Language Turin: Law Foundational Ontology Project: Space, Physics Ontology Works: Genetics, Molecular Biology Ontek Corporation: Biological Systematics Language and Computing: Natural Language Understanding

61 Recall: GDB: a gene is a DNA fragment that can be transcribed and translated into a protein Genbank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype

62 Ontology ‘fragment’, ‘region’, ‘name’, ‘carry’, ‘trait’, ‘type’... ‘part’, ‘whole’, ‘function’, ‘inhere’, ‘substance’ … are ontological terms in the sense of traditional (philosophical) ontology

63 BFO not just a system of categories but a formal theory with definitions, axioms, theorems designed to provide the resources for reference ontologies for specific domains of sufficient richness that terminological incompatibilities can be resolved intelligently rather than by brute force

64 Two basic oppositions Granularity (of molecules, genes, cells, organs, organisms...) SNAP vs. SPAN

65 SNAP vs. SPAN Two different ways of existing in time: continuing to exist (of organisms, their qualities, roles, functions, conditions) occurring (of processes) SNAP vs. SPAN = Anatomy vs. Physiology

SNAP: Entities existing in toto at a time

67 Three kinds of SNAP entities 1.Independent: Substances, Objects, Things 2.Dependent: Qualities, Functions, Conditions, Roles 3.Spatial regions

SNAP: Dependent

SNAP-Spatial Region

SNAP-Independent

71 SPAN: Entities occurring in time

72 SPAN: Dependent (Processes)

73 SPAN: Spatiotemporal Regions

74 Realization (SNAP-SPAN) the execution of a plan the expression of a function the exercise of a role the realization of a disposition the course of a disease the application of a therapy

75 SNAP dependent entities and their SPAN realizations plan function role disposition disease therapy SNAP

76 SNAP dependent entities and their SPAN realizations execution expression exercise realization course application SPAN

77 More examples: performance of a symphony projection of a film expression of an emotion utterance of a sentence increase of body temperature spreading of an epidemic extinguishing of a forest fire movement of a tornado

78 BFO = SNAP/SPAN + Theory of Granular Partitions + theory of universals and instances theory of part and whole theory of boundaries theory of functions, powers, qualities, roles theory of environments, contexts theory of spatial and spatiotemporal regions

79 MedO: medical domain ontology universals and instances and normativity theory of part and whole and absence theory of boundaries/membranes theory of functions, powers, qualities, roles, (mal)functions, bodily systems theory of environments: inside and outside the organism theory of spatial and spatiotemporal regions: anatomical mereotopology

80 MedO: medical domain ontology theory of granularity: relations between molecule ontology gene ontology cell ontology anatomical ontology etc.

81 IFOMIS project collaborate with L&C to show how an ontology constructed on the basis of philosophical principles can help in overhauling and validating L&C’s large terminology-based medical ontology LinkBase ®

82 Testing the BFO/MedO approach within a software environment for NLP of unstructured patient records collaborating with Language and Computing nv (

83 L&C LinKBase®: world’s largest terminology-based ontology with mappings to UMLS, SNOMED, etc. + LinKFactory®: suite for developing and managing large terminology-based ontologies

84 LinKBase lacking a formal theory LinKBase still lacking a formal theory BFO and MedO designed to add better reasoning capacity domain-entitiesby tagging LinKBase domain-entities with corresponding BFO/MedO categories according to the theory of granular partitionsby constraining links within LinKBase according to the theory of granular partitions

85 L&C’s long-term goal Transform the mass of unstructured patient records into a gigantic medical experiment

86 IFOMIS’s long-term goal Build a robust high-level BFO-MedO framework THE WORLD’S FIRST INDUSTRIAL- STRENGTH PHILOSOPHY which can serve as the basis for an ontologically coherent unification of medical knowledge and terminology

87 END