Formal Ontology and Information Systems

1
Formal Ontology and Information Systems

• Barry Smith
• http//ifomis.de

2

• Institute for Formal Ontology and Medical
  Information Science
• (IFOMIS)
• Faculty of Medicine
• University of Leipzig
• http//ifomis.de

3
The Idea

• Computational medical research
• will transform the discipline of medicine
• but only if communication problems can be solved

4
Database standardization

• is desparately needed in medicine
• to enable the huge amounts of data
• resulting from trials by different groups
• to be fused together

5
How make one system out of all of this?

• How resolve incompatibilities?
• ONTOLOGY the solution of first resort
• (compare kicking a television set)
• But what does ontology mean?
• Current answer a collection of terms and
  definitions satisfying constraints of description
  logic application ontology

6
Description logic

• a decidable logic (thus much weaker than
  first-order predicate logic) for manipulating
  hierarchies of concepts/general terms)

7
Enterprise Ontology

• A Sale is an agreement between two Legal-Entities
  for the exchange of a Product for a Sale-Price.
• A Strategy is a Plan to Achieve a high-level
  Purpose.
• A Market is all Sales and Potential Sales within
  a scope of interest.

8
Gene Ontology

• Molecular Function Ontology tasks performed by
  individual gene products
• examples transcription factor, DNA helicase
• Biological Process Ontology broad biological
  goals accomplished by ordered assemblies of
  molecular functions
• examples mitosis, purine metabolism
• Cellular Component Ontology subcellular
  structures, locations, and macromolecular
  complexes
• examples nucleus, telomere

9
Example from Molecular Function Ontology

• hormone GO0005179
• digestive hormone GO0046659
• peptide hormone GO0005180 adrenocorticotrop
  in GO0017043 glycopeptide hormone
  GO0005181 follicle-stimulating hormone
  GO0016913
• IS A

10
as tree (joined by is a links)

• hormone
• digestive hormone peptide hormone
• adrenocorticotropin
  glycopeptide hormone
• 
  follicle-stimulating hormone

11
Problem There exist multiple databases

• genomic
• cellular
• structural
• phenotypic
• and even for each specific type of information,
  e.g. DNA sequence data, there exist several
  databases of different scope and organisation

12
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
• (from Schulze-Kremer)

13
What is blood?

• Unified Medical Language System (UMLS)
• blood is a tissue
• Systematized Nomenclature of Medicine (SNOMED)
• blood is a fluid

14
Another Example Statements of Accounts

• Company Financial statements may be prepared
  under either the (US) GAAP or the (European) IASC
  standards
• These allocate cost items to different
  categories depending on the laws of the countries
  involved.

15
Job

• to develop an algorithm for the automatic
  conversion of income statements and balance
  sheets between the two systems.
• Not even this relatively simple problem has been
  satisfactorily resolved
• why not?

16
The World Wide Web

• Vast amount of heterogeneous data sources
• Needs dramatically better support at the level
  of metadata
• The ability to query and integrate across
  different conceptual systems
• The currently preferred answer is The Semantic
  Web, based on description logic
• will not work
• How tag blood?

17
Application ontology

• cannot solve the problems of database integration
• There can be no mechanical solution to the
  problems of data fusion in a domain like medicine

18
Applications ontology

• grew out of work in AI and in knowledge
  representation
• Ontologies are applications running in real time

19
Applications ontology

• ontologies are inside the computer
• thus subject to severe constraints on expressive
  power
• (effectively the expressive power of description
  logic)

20
Applications ontology cannot solve the
data-fusion problem

• because of its roots in knowledge mining

21
different conceptual systems
22
need not interconnect at all
23
because of the limits of knowledge mining
24
we cannot make incompatible concept-systems
interconnect
just by looking at concepts, or knowledge we
need some tertium quid
25
Applications ontology

• has its philosophical roots in Quines doctrine
  of ontological commitment and in the internal
  metaphysics of Carnap/Putnam
• Roughly, for an applications ontology the world
  and the semantic model are one and the same
• What exists what the system says exists

26
The Problem for the Quinean

• If an ontology is the set of ontological
  commitments of a theory, how can we cope with
  questions pertaining to the relations between the
  objects to which different theories are committed?

27
theories, semantic models, need not interconnect
at all
28
What is needed

• in some sort of wider common framework which is
  sufficiently rich and nuanced to allow concept
  systems deriving from different sources to be
  hand-callibrated

29
What is needed

• is not an applications ontology
• but
• a reference ontology
• (something like old-fashioned metaphysics)

30
Reference Ontology

• grew out of logic and analytic metaphysics
• An ontology is a theory of the relevant domain
  of entities
• Ontology is outside the computer
• seeks maximal expressiveness and adequacy to
  reality
• willing to sacrifice computational tractability
  for the sake of representational adequacy

31
Belnap

• it is a good thing logicians were around before
  computer scientists
• if computer scientists had got there first,
  then we wouldnt have numbers
• because arithmetic is undecidable

32
It is a good thing

• Aristotelian metaphysics was around before
  description logic,
• because otherwise we would have only hierarchies
  of
• concepts/universals/classes and no individual
  instances

33
Reference Ontology

• a theory of the tertium quid
• called
• reality
• needed to hand-callibrate database/terminology
  systems

34
Methodology

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

35
The Reference Ontology Community

• IFOMIS (Leipzig)
• Laboratory for Applied Ontology (Trento/Rome,
  Turin)
• Foundational Ontology Project (Leeds)
• Ontology Works (Baltimore)
• Ontek Corporation (Buffalo/Leeds)
• LandC (Belgium/Philadelphia)
• (CYC?)

36
Domains of Current Work in Reference Ontology

• IFOMIS Leipzig Medicine
• Laboratory for Applied Ontology
• Trento/Rome Ontology of Cognition/Language
• Turin Law
• Foundational Ontology Project (Leeds) Space,
  Physics
• Ontology Works (Baltimore) Genetics, Molecular
  Biology
• Ontek Corporation (Buffalo/Leeds) Biological
  Systematics
• LandC (Belgium/Philadelphia) Medical NLP
• (? CYC Everything ?)

37
Some Historical Background on Reference Ontology
38
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
• (from Schulze-Kremer)

39
Ontology

• Note that terms like fragment, region,
  name, carry, trait, type
• along with terms like part, whole,
  function, substance, inhere
• are ontological terms in the sense of traditional
  (philosophical) ontology

40
Aristotle
First ontologist

41
First ontology (from Porphyrys Commentary on
Aristotles Categories)
42
Linnaean Ontology
43
Formal Ontology

• term coined by Edmund Husserl
• the theory of those ontological structures
• such as part-whole, universal-particular
• which apply to all domains whatsoever

44
Edmund Husserl
45
Husserl outlines a new methodof constituent
ontology

• to study a domain ontologically
• is to establish the parts of the domain
• and the interrelations between them
• especially the dependence relations

46
Logical Investigations1900/01

• Aristotelian theory of universals and particulars
• theory of part and whole
• theory of ontological dependence
• the theory of boundaries and fusion

47
Formal Ontology

• contrasted with material or regional ontologies
• (compare relation between pure and applied
  mathematics)
• Husserls idea
• If we can build a good formal ontology, this
  should save time and effort in building reference
  ontologies for each successive domain

48
Basic Formal Ontology

• BFO
• The Vampire Slayer

49
Basic Formal Ontology

• Aristotelian theory of universals and instances
• theory of part and whole
• theory of ontological dependence
• theory of boundary, continuity and contact
• theory of states, powers, qualities, roles
  (SPQR-entities)
• theory of processes
• theory of environments/niches/contexts and
  spatial and spatio-temporal regions

50
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
• the latter should be of sufficient richness that
  terminological incompatibilities can be resolves
  intelligently rather than by brute force

51
Three types of reference ontology

• 1) formal ontology framework for rigorous
  definition of the highly general concepts such
  as object, event, whole, part employed in every
  domain
• 2) domain ontology, a top-level system with a few
  highly general concepts, applies formal ontology
  to a particular domain, such as genetics or
  medicine
• 3) terminology-based ontology, a very large
  system embracing many concepts and inter-concept
  relations

52
MedO medical domain ontology

• including sub-ontologies
• cell ontology
• drug ontology
• protein ontology
• gene ontology

53
other sub-ontologies

• anatomical ontology
• epidemiological ontology
• disease ontology
• therapy ontology
• pathology ontology
• the whole designed to give structure to the
  medical domain
• (currently medical education comparable to
  stamp-collecting)

54
MedO

• and its various sub-ontologies will inherit the
  definitions and axioms of BFO but will add new
  definitions and axioms of their own

55
Granularity

• cell ontology
• drug ontology
• protein ontology
• gene ontology
• imply that we need also a theory of granularity

56
Ontology

• like cartography
• must work with maps at different scales
• How fit these maps (conceptual grids) together
  into a single system?
• IFOMIS is developing a theory of granular
  partitions designed to provide a framework within
  which different maps/views of the same reality
  can be combined together

57
(No Transcript)
58
Part Two

• Reference Ontology
• and Situated Computing

59
Shimon Edelmans Riddle of Representation

• two humans, a monkey, and a robot are looking at
  a piece of cheese
• what is common to the representational processes
  in their visual systems?

60
Answer
The cheese, of course
61
Rodney Brooks

• Intelligence without Representation
• The world itself is our model
• opposition between the Engineering view and the
  SMPA View

62
SMPA model

• Sense Model Plan Act
• the agent first senses its environment through
  sensors
• then uses this data to build a model of the world
• then produces a plan to achieve goals
• then acts on this plan

63
Proposal

• SMPA belongs to the same methodological universe
  as Applications Ontology
• If we want to build an intelligent agent within
  this framework, there need to be representations
  of the domain within which the agent acts which
  are inside the computer

64
Engineering Approach

• The system embodies a number of distinct layers
  of activity (compare faculties of the mind)
• These layers operate independently and connect
  directly to the environment outside the system
• Each layer operates as a complete system that
  copes in real time with a changing environment
• Layers evolve through interaction with the
  environment (artificial insects/vehicles )

65
Brooks Engineering Approach

• lends very little weight to the role of
  representations or models
• At the same time it insists that AI should use
  the world in all its complexity in producing
  systems that react directly to the world
• An ontology appropriate for this approach would
  have to include within its purview both the world
  and the system,
• thus be essentially richer than the system alone

66
An intelligent system

• must be situated
• it is situatedness which gives the processes
  within each layer meaning
• meaning exists precisely in the relation to the
  world,
• the world serves also as to unify the different
  layers together and to make them compatible

67
Organisms, especially humans,

• fix their beliefs not only in their heads but in
  their worlds, as they attune themselves
  differently to different parts of the world as a
  result of their experience. And they pull the
  same trick with their memories, not only by
  rearranging their parsing of the world (their
  understanding of what they see), but by marking
  it.
• They place traces out there which changes what
  they will be confronted with the next time it
  comes around. Thus they don't have to carry their
  memories with them.
• Intelligence without Representation

68
Andy Clark, Being There

• humans can accomplish much without building
  detailed, internal models
• they rely on external scaffolding maps,
  models, tools, landmarks, buildings, language,
  culture
• we act so as to simplify cognitive tasks by
  "leaning on" the structures in our environment.

69
Compare the Ecological Psychology of J. J. Gibson

• To understand human cognition we should study
  the moving, acting human person as it exists in
  its real-world environment
• and taking account of how it has evolved into
  this real-world environment

70
For Gibson

• we are like (multi-layered) tuning forks tuned
  to the environment which surrounds us,
• and for us human beings this is a social
  environment which includes
• traces of prior actions in the form of records
  and representations

71
Gibsonian Ecological View of Information Systems

• To understand information systems we should study
  the hardware as it exists embedded in its
  real-world environment
• and taking account of the environment for which
  it was designed and built
• Information systems are like tuning forks they
  resonate in tune to their surrounding
  environments e.g. through their biological and
  chemical sensors

72
So what is the ontology of blood?
73
We cannot solve this problem just by looking at
concepts (by engaging in further acts of
knowledge mining)
74
concept systems may be simply incommensurable
75
the problem can only be solvedin
Brooksian/Gibsonian fashion
by taking the world itself into account
76
By looking not at concepts, representations,

• and their semantic models
• but rather at organisms acting in the world
• and standing at different levels in a range of
  different sorts of relations to the world

77
We then recognize

• that the same object can be apprehended at
  different levels of granularity
• at the perceptual level blood is a liquid (?)
• at the cellular level blood is a tissue

78
This implies a view of ontology

• not as a theory of concepts
• but as a theory of reality
• But how is this possible?
• How can we get beyond our concepts?
• answer ontology must be maximally opportunistic
• it must relate not to beliefs, concepts,
  syntactic strings but to the world itself

79
Maximally opportunistic

• means
• look at concepts and beliefs critically
• and always in the context of a wider view which
  includes independent ways to access the objects
  themselves
• at different levels of granularity
• and taking account of tacit knowledge of those
  features of reality of which the domain experts
  are not consciously aware

80
Maximally opportunistic

• means
• look not at what the expert says
• but at what the expert does
• Experts have expertise knowing how
• Ontologists can have windows on reality, by
  focusing on categories, and can extract some form
  of knowing that
• Gibsonianism experts dont know what the
  ontologist knows

81
Ontology must be maximally opportunistic

• This means
• dont just look at beliefs
• look at the objects themselves
• from every possible direction,
• formal and informal
• scientific and non-scientific

82
Maximally opportunistic

• means
• look at the same objects at different levels of
  granularity

83
Second step select out the good
conceptualizations

• these have a reasonable chance of being
  integrated together into a single ontological
  system
• based on tested principles
• robust
• conform to natural science

84
Ontology

• like cartography
• must work with maps at different scales

85
Medical ontologies

• at different levels of granularity
• cell ontology
• drug ontology
• protein ontology
• gene ontology
• anatomical ontology
• epidemiological ontology
• Rigidly hierachical, modular organization with
  many things which can go wrong

86
There are many compatible map-like partitions

• many maps at different scales,
• all transparent to the reality beyond

87
Partitions should be cuts through reality

• a good medical ontology should NOT be compatible
  with the conceptualization of disease as
• caused by evil spirits and demons and cured by

golems
88
Three main sorts of partitions

• 1. substances and their parts
• 2. qualities/functions/roles
• 3. processes
• in addition
• spatial regions/niches
• spatio-temporal regions
• AS UNIVERSALS, AS PARTICULARS

89
1. Substances and their parts

• Patterned parts (carved out by fiat)
• chess board
• football pitch
• Brocas Region
• nervous system

90
2. Functions

• function of a screwdriver
• tied to processes
• generalized four-dimensional shapes (carved
  out by fiat)
• contextual dependence
• function of the heart
• function of the circulatory system

91
Generalized 4-dimensional shapes

• as UNIVERSALS
• as PARTICULARS

92
Once we understand functions

• we can also understand malfunctions
• broken screwdriver
• defective heart

93
Application to Bodily Systems

• Immune system, digestive system
• are complex substances
• paradigm skeleton
• carved out by fiat from the whole organism in
  terms of their functions
• engaging in specific types of processes

94
Multi-layered systems

• How one system can use another system to exercise
  its function
• Drug transport system uses circulatory system
• (Layered Mereotopology
• of substances
• of processes)

95
Part 3
96
Testing the BFO/MedO approach

• within a software environment for NLP of
  unstructured patient records
• collaborating with
• Language and Computing nv (www.landc.be)

97
LC

• LinKBase worlds largest terminology-based
  ontology
• incorporating UMLS, SNOMED, etc.
• LinKFactory suite for developing and managing
  large terminology-based ontologies

98
LinKBase

• BFO and MedO designed to add depth, and so also
  reasoning capacity
• by tagging LinKBase terms with corresponding
  BFO/MedO categories
• ???