OntoClean Methodology

1
OntoClean Methodology

• As presented at AOS Workshop by
• Aldo Gangemi
• CNR-IP, Ontology and Conceptual Modelling Group

2
Credits

• CNR - Ontology and Conceptual Modelling Groups
• Nicola Guarino, Claudio Masolo, Alessandro
  Oltramari
• Nicola.Guarino,claudio.masolo,alessandro.oltramar
  i_at_ladseb.pd.cnr.it
• Aldo Gangemi, Domenico Pisanelli, Geri Steve
• gangemi,pisanelli,steve_at_itbm.rm.cnr.it
• Vassar College
• Chris Welty
• weltyc_at_vassar.edu
• Publications are retrievable on-line at the
  following URLs
• http//www.ladseb.pd.cnr.it/infor/ontology/ontolo
  gy.html
• http//saussure.irmkant.rm.cnr.it/onto/index.html
  

3
Some Applications so far

• ON9 ontology library
• UMLS Metathesaurus semantic mining
• Medical terminologies integration
• Integration of Clinical Guidelines Standards
• Ontological upgrading of Wordnet
• Ontological Web Agents
• Product ontologies
• OntoClean Top-Level
• Legal ontologies and norm dynamics
• Portal directories maintenance and subject
  ontology
• Content standards for the semantic web

4
Some Projects so far

• GALEN (EU AIM Project, academic/industrial
  project for a medical terminology server)
• SOLMC (Ontological and Conceptual Modelling
  Tools, CNR Special Project)
• Arianna Catalog (industrial pilot project to
  build and maintain portal directories)
• IKF, Intelligent Knowledge Fusion (Eureka Project
  E!2235, academic/industrial project for
  information integration)
• IIDEAS, Integration of Industrial Data for
  Exchange, Access, and Sharing
• IEEE Standard Upper Ontology Study Group
• OntoWeb Ontology-based information exchange for
  knowledge management and electronic commerce (EU
  Network of Excellence)
• OntoWeb SIG on Content Standards
• TICCA (Italian Project, Cognitive technologies
  for artificial agents)

5
OntoClean Antecedents

• Guarino and Weltys theoretical tools for the
  ontological refinement of taxonomies
• ONIONS techniques for domain ontology development
  and large-scale terminology integration

6
OntoClean Components

• Formal Criteria
• Top-Level Ontology
• Ontology of Universals
• Domain-Level Development Guidelines
• Applications

7
OntoClean Components

• Formal Criteria (brief explanation)
• Top-Level Ontology
• Ontology of Universals
• Domain-Level Development Guidelines
• Applications

8
Individuals and Concepts

• The term "meta-property" adopted here is based on
  a fundamental distinction within the domain of
  discourse
• individuals or particulars vs.
• concepts or universals
• Meta-level properties induce distinctions among
  concepts, while object-level properties induce
  distinctions among individuals

9
Rigidity

• A property is essential to an individual iff it
  necessarily holds for that individual
• A property is rigid (R) iff, necessarily, it is
  essential to all its instances. A property is
  non-rigid (-R) iff it is not essential to some of
  its instances, and anti-rigid (R) iff it is not
  essential to all its instances
• Person vs Student

10
Identity

• A property carries an identity criterion (I) iff
  all its instances can be (re)identified by means
  of a suitable sameness relation. A property
  supplies an identity criterion iff such criterion
  is not inherited by any subsuming property
• Person vs. Student

11
Dependence

• An individual x is constantly dependent on y iff,
  at any time, x can't be present unless y is fully
  present, and y is not part of x. Ex Hole/Host
• A property P is constantly dependent (D) iff,
  for all its instances, there exists something
  they are constantly dependent on.
• Here Dependent Constantly Dependent

12
Types vs. Roles

• A rigid property that supplies an identity
  criterion and is not (notionally) dependent is
  called a type.
• An anti-rigid property that is notionally
  dependent is called a role. It is a material role
  if it carries (but not supplies) an identity
  criterion, and a formal role otherwise.
• Person vs. Student vs. Part

13
Extensionality

• An individual is said to be extensional iff,
  necessarily, everything that has the same proper
  parts is identical to it
• A property is extensional (E) iff, necessarily,
  all its instances are extensional
• A property is anti-extensional (E) iff,
  necessarily, all its instances are
  non-extensional, so that they can possibly change
  some parts while keeping their identity

14
Concreteness

• An individual is concrete iff it has a physical
  location. A property whose instances are
  necessarily concrete will be marked with the
  meta-property C
• Note that an individual can be concrete without
  being necessarily real, or actual Peter Pan is
  not real but is concrete
• This meta-property is a bit less formal (in the
  ontological sense) than the previous ones, since
  it makes an ontological commitment towards the
  existence of physical (spatial, temporal or
  spatio-temporal) locations. We see physical
  locations as primitive qualities that individuals
  can have

15
Unity

• An individual is unified by a (suitably
  constrained) relation R iff it is a mereological
  sum of entities that are bound together by R. Ex.
  the relation having the same boss may unify a
  group of employees in a company
• An individual w is a whole under R iff it is
  maximally unified by R, in the sense that R is
  internal to w, and no part of w is linked by R to
  something that is not part or w
• A property P is said to carry unity (U) if there
  is a common unifying relation R such that all the
  instances of P are essential wholes under R. A
  property carries anti-unity (U) if all its
  instances can possibly be non-wholes. If every
  instance of P is an essential whole, but there is
  no unifying relation common to all instances of
  P, then we mark P with the property U

16
Singularity and Plurality

• An individual is a singular whole iff its
  unifying relation is the transitive closure of
  the relation "strong connection", like that
  existing between two 3D regions that have a
  surface in common. Topological wholes of this
  kind have a special cognitive relevance, which
  accounts for the natural language distinction
  between singular and plural
• A plural individual is a sum of singular wholes
  that is not itself a singular whole. Plural
  individuals may be wholes themselves or not. In
  the former case they will be called collections
  in the latter case pluralities
• A piece of coal is a singular whole. A lump of
  coal is a topological whole, but not a singular
  whole, since the pieces of coal merely touch each
  other, with no material connection. It is
  therefore a plural whole

17
Applying Formal Properties

• If a property holds necessarily for all the
  instances of a certain concept, of course its
  negation cannot hold necessarily for all the
  instances of a subsumed concept.
• Then, if F is a certain formal property, anti-F
  cannot subsume F anti-rigidity cannot subsume
  rigidity, anti-unity cannot subsume unity, and
  anti-extensionality cannot subsume
  extensionality.
• After labeling every concept in a taxonomy with
  its formal properties, we can easily check its
  ontological consistency

18
OntoClean Components

• Formal Criteria
• Top-Level Ontology
• Ontology of Universals
• Domain-Level Development Guidelines
• Applications

19
The OntoClean Top-Level Ontology an Overview
20
Basic Design Guidelines for the OntoClean
Top-Level

• Introduction of ontological categories lying
  behind Natural Language and Human Commonsense
• Use of formal properties (general and neutral as
  possible) to characterize the ontological
  categories
• Rigidity, Identity, Dependence, Unity,
  Extensionality, Singularity, Concreteness (see
  next slide for references)
• Refinement of top-distinctions by further
  analysis (taking into account philosophy,
  cognitive sciences, linguistics,)
• IMPORTANT All top-concepts are considered to be
  rigid, as they
  are assumed to reflect essential
  properties of their instances

21
Brand New Essential Bibliography

• Guarino and Welty 2001,
• Supporting Ontological Analysis of Taxonomic
  Relationships (Data and Knowledge Engineering -
  in press)
• Identity and Subsumption (In R.Green, C. Bean and
  S.Myaeng eds., The Semantics of Relationships
  an Interdisciplinary Perspective. Kluwer - in
  press)
• Gangemi, Guarino, Masolo, Oltramari 2001,
• Understanding Top-Level Ontological Distinctions
  ( Proceedings of IJCAI 2001 workshop on
  Ontologies and Information Sharing)
• Gangemi, Guarino, Oltramari 2001,
• Conceptual Analysis of Lexical Taxonomies The
  Case of WordNet Top-Level (Proceedings of FOIS
  2001)

22
(1) The Top-Level Unique Beginners, Direct
Hyponyms, Some Synsets from WordNet 1.6

• Aggregate (D, U)
• Amount of matter (E)
• Arbitrary collections
• Object (D, U)
• Extensional Body (E)
• Ordinary Object (E)
• Event (D, E)
• Feature (D, U, -E)
• Relevant part
• Dependent Region

• body substance, mixture1, mass5
• universe1, elementary particle
• artifact, land4,(unitary) collection1
• phenomenon, act2, state4
• edge3, skin1, paring2
• opening10, excavation3

23
(2) The Top-Level Unique Beginners, Direct
Hyponyms, Some Relevant Synsets from WordNet 1.6

• Abstraction (C)
• Abstract entity
• Proposition
• Set
• ...
• Quality space
• Color space
• Shape space
• Quality (D,E,U)
• Color
• Shape
• ...

• conclusion5, lemma1
• union7, singleton2
• chromatic color
• shape2

24
(1) Aggregate vs. Object

• What distinguishes an object from an aggregate is
  that the former is an essential whole, namely it
  has a unity criterion, while the latter is not.
  For example, John can make-up a snowman
  (object) starting from the scattered snow (amount
  of matter) covering his courtyard, adding a hat,
  a carrot, two deadwoods, etc. In general, amounts
  of matter are mass-nouns (you cant say a snow, a
  water, ...), while objects are count-nouns (such
  as a snowman, five glasses of water,and so on).

25
(2) Aggregate vs. Object

• Arbitrary collections are just mere sum of wholes
  which are not themselves essential wholes (as the
  collection of goods in a bazar). In this sense,
  they are kinds of aggregate. On the other hand,
  there are collections which are themselves
  essential wholes, as a library. In our top-level
  these unitary collections are to be conceived as
  a specialization of the object category.

26
(3) Aggregate vs. Object

• An object can change some parts, keeping or not
  its identity. In the first case, we call it
  Ordinary Object (E), in the second case
  Extensional Object (E). My car will continue to
  be the same even if I replace one of its wheels.
  On the contrary, if I consider the universe,
  removing a single elementary particle I wont
  have the universe any more, but a different
  entity.
• Regarding aggregates, we can say that amounts of
  matter are clearly E, while arbitrary
  collections can be considered as
  pseudo-extensional (changes in the parts of a
  member of a collection may be allowed).

27
Event

• Events occur in time. They are assumed to be
  dependent (D) on those objects (D) that are
  their partecipants.
• The penalty kick by Roberto Baggio (?main
  partecipant)
• Partecipants are not parts of events. Parts of
  events can be
• temporal (the first movement of a symphony)
• spatial (the strings playing within a symphony)
• Parts of events are always essential, which means
  that events are extensional (E).
• Our taxonomy of events needs to be
  improved and populated. A comparison
  with EuroWordNet and SIMPLE, in
  this sense, may be useful.

28
Feature

• Features are parasitic (D) entities, that
  exist insofar their host exists. Features may be
  relevant parts of their host, like a bump in a
  road, or dependent regions, such as a hole in a
  piece of cheese, the underneath of a table, or
  the shadow of a tree (which are not parts of
  their hosts). All features are essential wholes,
  but no common unity criterion may exist for all
  of them (U). Some features can change parts
  keeping their identity, while some others not
  for this reason, we use -E as the common formal
  property (E and E are both subsumed by -E).

29
Abstraction

• Abstractions are entities that are not concrete,
  that is, they do not have a physical location
  (C). Quality spaces are the first examples of
  abstractions time, geometric space, length,
  color, are all conceptual spaces, with different
  topological structure. Terms like red, long,
  sweet, old, recent etc. correspond to regions in
  a quality space. We can therefore describe the
  structure of a quality space with a first-order
  theory, using topological notions for instance,
  we can say that red is adjacent to brown. Other
  examples of Abstraction are propositions, sets,
  symbols, etc.

30
Quality (1)

• Qualities are always qualities of something in
  this sense, they are dependent (D). Qualities
  are individual, i.e. that they are inherent to a
  unique entity (the color of this rose is red). We
  call quality-type every homogenous group of
  individual qualities, such as color, shape,
  volume, etc. In the OntoClean top-level qualities
  are structured in strict relationship with
  quality spaces every quality-type corresponds
  to a quality space in the branch of Abstractions.
  A region in a quality space corresponds to an
  individual quality of an entity in our
  conceptualization of the world.

31
Quality (2)

• Following our approach, the red of the rose on
  the right figure is represented as located in a
  certain region in the colors-quality-space. In
  the same way, the spherical shape of the ball
  below is represented as located in a certain
  region of the shape-quality-space. In principle,
  time and space could be treated as qualities too.
  We are currently studying the ontological
  commitments and the formal properties concerning
  this options.

32
Appendix An Alternative View of the OntoClean
Top-Level (1)

• Since the agreement on the meaning of general
  categories is not always easy, in this short
  presentation we preferred to make clear first the
  most relevant top-level concepts, leaving aside
  the various ways they can be presented in a
  hierarchy. For example, we could have introduced
  the OntoClean top-level by considering the
  general distinction between concrete and abstract
  entities as the complete partition of what there
  is in the world. Then, within concrete entities
  we could have distinguished independent from
  dependent entities. The overall taxonomy would
  have been like this

33
Appendix An Alternative View of the OntoClean
Top-Level (2)

• Quality (E,U)
• Color
• Shape
• Abstraction (C)
• Abstract entity
• Proposition
• Set
• ...
• Quality space
• Color space
• Shape space

• Concrete (C)
• Independent (D)
• Aggregate ( U)
• Amount of matter (E)
• Arbitrary collections
• Object ( U)
• Extensional Body (E)
• Ordinary Object (E)
• Dependent (D)
• Event ( E)
• Feature ( U, -E)
• Relevant part
• Dependent Region

34
OntoClean Components

• Formal Criteria
• Top-Level Ontology
• Ontology of Universals (list of main kinds)
• Domain-Level Development Guidelines
• Applications

35
Which part are you talking about?

• If my liver is part of my digestive system, and
  that system is part of me, is my liver part of
  me?
• If my liver is a part of me and I am part of the
  CNR, is my liver part of the CNR?
• My liver is a component of my digestive system,
  while I am a member of CNR. No rule for composing
  component and member relations
• Moreover, I am a body, but I am also a person. A
  living person depends on a body. Nevertheless, a
  living person can be a member of CNR, but a body
  cannot

36
Ontology of Universals - Main Relation Kinds -

• Intracategorial
• Mereological (entity, entity)
• Topological (entity, entity)
• Intercategorial
• Localization (region, entity)
• Participation (event, entity)
• Representation (sign, entity)
• Entrenched axiomatic characterisation

37
OntoClean Components

• Formal Criteria
• Top-Level Ontology
• Ontology of Universals
• Domain-Level Development Guidelines (hints)
• Applications

38
Kinds of Terminological Ontology Sources

• Catalog of normalized terms, e.g. a list of terms
  used in the reports from a laboratory no
  taxonomy, no axioms, and no glosses
• Glossed catalog, e.g. a dictionary a catalog
  with glosses.
• Thesaurus, e.g. many parts of the UMLS
  Metathesaurus, GEMET a hierarchical collection
  of terms the hierarchical link is usually
  polysemous
• Taxonomy, e.g. the ICD10 a collection of classes
  with a partial order induced by inclusion
  (classification)
• Axiomatized taxonomy, e.g. the GALEN Core Model
  a taxonomy with axioms
• Ontology library, e.g. the Ontolingua repository
  a set of axiomatized taxonomies with relations
  among them. Each element of the library is a
  module, which can be included into another one.
  Also, a concept from a module can be only used
  into another one. Ontology modules can be
  considered subdivisions of the namespace of a
  model

39
Impairments in Traditional Terminologies

• Lack of hierarchies
• Ambiguous hierarchies
• Informality
• Lack of modularity or cyclic taxonomical
  dependencies between modules
• Polysemy of various sorts
• Uncertain semantics
• Ontological opaqueness
• Lack of a (minimal) set of axioms
• 'Remainder' partitions
• 'Exception' partitions
• Terminological cycles
• Meta-level soup (individuals mixed up with
  universals or even higher order concepts)
• Low maintenance capabilities

40
Ontologies some desiderata

• An explicit taxonomy with subsumption among
  concepts
• Semantic explicitness of relations
• Rigorous modularity of namespace
• A stratified design of the modules
• Absence of polysemy within a module
• Disjointness of rigid concepts within a module
  and within the top-level
• A proper interface between the ontology namespace
  and one or more sets of lexical realizations
• Linguistically meaningful naming policy
  (cognitive transparency)
• Rich documentation
• Some (minimal) axiomatization to detail the
  difference among sibling concepts
• Explicit linkage to concepts and relations from
  generic theories
• Meta-level assignments to distinguish among the
  formal primitives assigned to concepts
• Languages and implementations that support the
  previous needs as well as the possibility of
  collaborative modeling

41

ONtologic Integration Of Naïve Sources

42
Lexical and Linguistic Analysis

• Morpho-semantic analysis (extraction of
  sematically meaningful units)
• Functional informational structures (extraction
  of head/modifier syntagmatic structures)
• Conceptual polysemy treatment (templates for
  systematic ambiguity resolving)

43
Conceptual Issues in Ontology Integration

• Ontology integration is generally speaking
  the construction of an ontology C that formally
  specifies the union of the vocabularies of two
  other ontologies A and B
• To be sure that A and B can be integrated at some
  level, C has to commit to both A's and B's
  conceptualizations. In other words, the intension
  of the concepts in A and B should be mapped to
  the intension of C's concepts
• Unfortunately, this cannot be realized using only
  the conceptual relations specified in A and B for
  local tasks (for a specific context). The
  methodological principle adopted here is that
  generic ontologies reused from the philosophical,
  linguistic, mathematical, AI literature must
  found the comparison of different intensions. Our
  approach may be called principled conceptual
  integration

44
Ontology Library Architecture
45
Aspects of integration

• Three aspects of an ontology are taken into
  account
• the intended models of the conceptualizations of
  its vocabulary
• the domain of interest of such models, i.e. the
  'topic' of the ontology
• the namespace of the ontology
• The most interesting case is when A and B are
  supposed to commit to the conceptualization of
  the same domain of interest or of two overlapping
  domains. In particular, A and B may be

46
The main steps (I)

• 0. Semantically opaque hierarchies and lists are
  pre-processed in order to create clean
  taxonomies
• 1. All concepts, relations, templates, rules, and
  axioms from a source ontology are represented in
  the ONIONS formalisms, currently Loom,
  Ontolingua, and OKBC
• 2. When available, plain text descriptions are
  analyzed and axiomatized (text formalization)
• 3. The union of such products is integrated by
  means of a set of generic ontologies. This is the
  most characteristic activity in ONIONS, which can
  be briefly described as follows

47
II

• 3.1. For any set of sibling concepts in a
  taxonomy, the conceptual difference between each
  of them is inferred, and such difference is
  formalized by axioms that reuse the relations and
  concepts already in the library. If no concept is
  available to represent the difference, new
  concepts are added to the library
• 3.2. For any set of polysemous senses of a term,
  different concepts are stated and placed within
  the library according to their topic and to the
  available modules. (Polysemy occurs when two
  concepts with overlapping or disjoint intended
  models have the same name.)
• 3.3. Often, polysemous senses of a term - as well
  as different 'alternative' concepts - are
  metonymically related. For example
  process/outcome (as in inflammation),
  region/object (as in body region), etc.
  Alternatives must be properly defined by making
  it explicit the relationship between them e.g.
  "has-product" for inflammation, "location" for
  body-region
• 3.4. When stating new concepts, the relations
  necessary to maintain the consistency with the
  existing concepts are instantiated. If conflicts
  arise with existing theories, a more general
  theory is searched which is more comprehensive.
  If this is impracticable, an alternative theory
  is created

48
III

• 3.5. Relevant integration cases. Since ONIONS
  requires the use of generic theories to
  axiomatize alternative theories, the integration
  of a concept C from an ontology O is performed by
  comparing C with the concepts D1,,n already
  present in the evolving ontology library L, whose
  ontology set M1,,n contains at least a
  significant subset of generic ontologies and the
  set of domain ontologies at that state in the
  evolution of L. The following cases appear
  relevant to the methodology
• 3.5.1. C's name is polysemous in O (internal
  polysemy). Iterate 3.2 3.4
• 3.5.2. C's name is homonym with the name of a Di.
  (Homonymy occurs when both the intended models
  and the domains of two concepts with the same
  name are disjoint.) Homonyms must be
  differentiated by modifying the name, or by
  preventing the homonyms to be included in the
  same module namespace
• 3.5.3. C's name is synonym with the name of a Di.
  (Synonymy is the converse of homonymy and occurs
  when two concepts with different names have both
  the same intended model and the same domain.)
  Synonyms must be preserved, or included in the
  set of lexical realizations related to the
  concept
• 3.5.4. C is subsumed by some Di in L, but it has
  no total mapping on any Dj in L. The gap in L
  must be filled by adding C as a subconcept of Di

49
IV

• 3.5.5. C is an intersection between two concepts
  Di and Dj in L. Solved by distinguishing types
  and roles, or different defining elements
• 3.5.6. C has an alternative concept Di in L (same
  domain, but overlapping or disjoint intended
  models)
• 3.5.6.1. If C metonymically depends on Di, C is
  properly related to Di
• 3.5.6.2. If C and Di are different viewpoints on
  the same domain of interest, both concepts are
  kept if the case, they are included in separate
  modules
• 3.5.6.3. If the intended model of C is finer than
  Di's, Di is substituted with C
• 3.5.6.4. If the intended model of C is coarser
  than Di's, C is ignored (but track of it is kept
  for mapping between sources)

50
V

• 4. The library of generic, intermediate, and
  domain ontologies should be stratified, say
  domain modules should include intermediate
  modules - that should include generic modules -
  so that each set of modules can be plugged or
  unplugged from its more general set without
  affecting the coherence of the entire library
• 5. The source ontologies are explicitly mapped to
  the integrated ontology, in order to allow
  interoperability. The only admitted mappings are
  equivalent and coarser equivalent. Formally for
  any source ontology SO and an ontology IO that is
  supposed to result (also) from the integration of
  SO, for any concept Ci in SO, there is a Di in IO
  such that CiI DiI (equivalence of possible
  interpretations), or there is a disjunctive
  concept (or Di Dj) in IO such that CiI DiI ?
  DjI (equivalence of possible interpretations to a
  disjunction of concepts i.e. to a union of
  finer concepts)
• 5.1. Partial mappings must have been already
  resolved through the methodology if any, some
  step in the integration procedure must be
  iterated

51
Formal Approach

• Ontology Integration Framework (Calvanese,DeGiacom
  o,Lenzerini 2001)
• Description Logic-based
• Mapping relations between local ontologies
• Views either globally or locally

52
OntoClean Components

• Formal Criteria
• Top-Level Ontology
• Ontology of Universals
• Domain-Level Development Guidelines
• Applications (examples)

53
The OntoWordnet Loom KB

• We created a Loom KB, containing, for each named
  concept, its direct super-concept(s), some
  annotations describing the quasi-synonyms, the
  gloss and the synset subject assignment, and its
  original numeric identifier in WordNet for
  example
• (defconcept HorseEquus_Caballus
• is-primitive EquineEquid
• annotations ((subject animals)
• (word horse)
• (word Equus caballus)
• (documentation "solid-hoofed herbivorous
  quadruped domesticated since prehistoric times"))
• identifier 101875414)

54
What can be improved in WordNet

• Top-level not based on formal ontological
  principles
• Concepts vs. individuals lack of distinction
• Object-level vs. meta-level lack of
  distinction
• Roles vs. types lack of distinction
• Taxonomies to be revised
• Multihierarchies
• Few conceptual relations among synsets
• Weak subdivision by subject
• Phrases to be augmented

55
Concepts vs. individuals

• Under Event, we find Fall (of mankind)
• Under Territorial_Dominion we find Macao
• Problem gt Expressivity lack
• Solution gt We need an instance-of relation

56
Object-level vs. Meta-level

• The synset Abstraction_1 includes both abstract
  entities, such as Set, Time, and Space, and
  abstractions such as Attribute, Relation,
  Quantity.
• Abstraction_1 is glossed as "a general concept
  formed by extracting common features from
  specific examples. Abstraction seems to be
  intended therefore as a psychological process of
  generalization. This meaning seems to fit the
  latter group of terms (Attribute, Relation,
  Quantity), but not the former, which would be
  considered as abstract under a different notion,
  namely not being extended in space/time.
• Solution
• Attribute, Relation, and Quantity are meta-level
  concepts, while Set, Time, and Space are
  object-level concepts.

57
Role vs. Type

• RULE gt A role cannot subsume a type
• Person (that we consider as a type) is subsumed
  by two different concepts, Organism and
  Causal_Agent.
• Organism can be conceived as a type as well,
  while Causal_Agent as a formal role (inferrable
  from its subconcepts)
• The first subsumption relationship is therefore
  feasible, while the second one shows a rigidity
  violation (roles are not rigid)
• Solution gt Maintain meta-property-based views of
  taxonomy

58
Taxonomical Ambiguity in Wordnet

• Apparently sibling noun phrases are located in
  distant taxonomy branches

• Solution dissolve heterogeneous branchings by
  analysing complex senses (knowledge is a type,
  communication is a role, social relation is a
  meta-level concept, etc)

59
Wordnets Top Synsets (UBs and some hyponyms)
60
Some more examples

• Possession_1 is a role, and it includes both
  roles and types
• Some hyponyms of Physical_Object are mapped to
  Feature
• Abstraction_1 is the most heterogeneous Unique
  Beginner. It contains abstracts (Set_5), quality
  spaces (Chromatic_Color), qualities (mostly from
  the synset Attribute) and a hybrid concept
  (Relation_1) that contains abstracts, other
  entities, and even meta-level categories
• Psychological_Feature contains both abstract
  entities (Cognition) and Events (Feeling_1)
• Event_1, Phenomenon_1, State_1, Act are globally
  mapped to our Event category, although by
  simply looking at their children it seems quite
  hard to explicit any criteria to maintain the
  original distinctions

61
Cleaned-Up WordNet

• Following OntoClean, we have concentrated first
  on the so-called backbone taxonomy, which only
  includes the rigid properties. Formal and
  material roles have been therefore excluded from
  this preliminary work
• An extreme heterogeneity appears evident
• Ex. Entity seems a catch-all class
  (Imaginary_place, Anticipation, Inessential,
  Location, Object, Causal_Agent, etc.)
• Therefore, we also decided to exclude from the
  top-level cleaning those synsets sharing very few
  hyponyms (resembling orphans), like some of the
  above hyponyms of Entity

62
Revised Top-Level Table
63
Main Types in the Revised Top-Level
64
Ontological Integration Applied to Medical
Terminologies

• UMLS Metathesaurus Category Intersections
• UMLS Semantic Network
• SnoMed Nomenclature
• ICD10 Classification
• MeSH Thesaurus

65
Some UMLS concepts pertaining the intersection
Amino Acid, Peptide, or Protein Carbohydrate

• (hamster oviduct-specific glycoprotein)
• (Par j I)
• ((Man)6(GlcNAc)2Asn)
• (Zn(2)-IAA)
• (collapsing factor)
• (BDV 18K glycoprotein)
• (SI-gene-associated glycoprotein, Nicotiana)
• (FdI allergen)
• (sca gene product)
• (EPV20 protein)
• (lubricin)
• (Pluritene)
• (Par h 1 allergen)
• (Wnt11 gene product)
• (I-D-Gal-BSA)
• (mannose-bovine serum albumin conjugate)
• (acrosome granule lysin)
• (sulfatide activator)
• (vaccinia virus A34R protein)

gt More than 118,000 UMLS concepts (25) are
classified under an intersection
66
Ontological analysis of the intersection (Loom DL
syntax)

• (defconcept Amino Acid, Peptide, or Protein
  Carbohydrate
• "834 instances. This conjunct includes two
  sibling types.
• A protein containing a carbohydrate."
• annotations ((Sugg.Name "carbohydrate-containin
  g-protein")
• (onto-status integrated))
• is-primitive (and protein
• (some has-component carbohydrate))
• context substances)

67
Medical Morphologies

• Names of anatomical morphologies are often
  polysemous
• Both a condition and the function that caused the
  condition ("inflammation", "ulcer", "fracture",
  "wound", "hyperplasia")
• Both an object and the function that produced the
  object ("neoplasm", "hemorrhage")
• Both an object O and the condition created in
  another object O' by O ("obstruction")
• For example "the fracture has been caused by a
  fall" vs. "the fracture is transverse" "the
  obstruction occurred in the jejunum" vs. "the
  obstruction has been removed"
• Conceptual analysis puts into evidence other
  issues concerning morphologies
• The dependence between a morphological condition,
  a function, and the related organ. For example,
  an "ulcer" (as a condition) of a stomach implies
  that the stomach embodies an ulceration function
  (an ulcer as a function)
• The mereological import of morphologies some are
  featured by an organ, some only by a part of an
  organ. For instance, an "ectopic heart" is wholly
  ectopic, but an "ulcerated stomach" is only
  partly ulcerated

68
Morphologies Analyzed

• a quality space ("color", "consistency",
  "thickness", "size", "number", "shape")
• a situation
• a topologically relevant condition
• an alteration of connection
• that creates a configuration (a new property) in
  an object ("fracture", "wound")
• in the holey interior of an object
  ("obstruction")
• between several objects ("fusion")
• an alteration of the boundary between an object
  holey interior and the object complement
• creating a configuration in the boundary
  ("cavitation", "ulcer")
• producing a substance flow ("hemorrhage",
  "ulcer")
• an abnormal placement ("dislocation", "ectopia",
  "absence")
• a form alteration condition ("deformity",
  "hyperplasia", "hypoplasia")
• a condition involving the alteration of several
  properties ("inflammation", "eruption")
• an abnormal, foreign object ("mass", "neoplasm",
  "calculus", "obstruction")

69
Use OntoClean for all your ontology cleaning
needs!