some comments on granularity scale & collectivity by rector & rogers thomas bittner ifomis...
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Some comments on
Granularity Scale & Collectivity
by Rector & Rogers
Thomas Bittner
IFOMIS Saarbruecken
Overview
• Problems with doing ontology using DLs
• Problems with collectives
• Problems with indeterminacy
• Problems with transitivity
• Conclusions
Problems with doing ontology using Description Logics
Ontologies constrain intended meaning
The biomedical world
What you could say in L= Models of the language L
Language L(symbols+meaning)
We chose a language such that we canexpress the important aspects of theBio-medical world
This is what you actually say in your your ontology
The biomedical domain isamong the intended models= What you want to talk about
Ontologies constrain intended meaning
The biomedical world
Language L
Ontology
Models of the language L
Intended models
Guarino, 1998
Ontologies constrain intended meaning
GoodOntology
Guarino, 1998
Ontologies constrain intended meaning
BadOntology
Very badOntology
Guarino, 1998
Ontologies constrain intended meaning
BadOntology
Inappropriate tools which do not allow you to write good ontologies
• Mistakes when writing axioms• Too few axioms
Kinds of Ontology Languages
Kinds of Ontology Languages
Different degrees of expressive power for the specification of the intended meaning
A shared vocabulary plus a specification of its intended meaning
Meaning specified implicitly and informally in natural language
Two extremes
Kinds of Ontology Languages
Different degrees of rigor of the specification of the intended meaning
A shared vocabulary plus a specification of its intended meaning
Meaning specified implicitly andinformally in natural language
meaning specifiedexplicitly as a logical theory
Two extremes
In between a continuum of degree of expressive power
Kinds of Ontology Languages
Terms
General Logic
Thesauri
formalTaxonomies
Frames(Protege)
Data Models(UML, STEP)
Description Logics
(DAML+OIL)
Principled, informal
hierarchies
ad hoc Hierarchies
(Yahoo!)structured Glossaries
XML DTDs
Data Dictionaries
(EDI)
‘ordinary’Glossaries
XML Schema
DB Schema
Glossaries & Data Dictionaries
MetaData,XML Schemas, & Data Models
Formal Ontologies & Inference
Thesauri, Taxonomies
Michael Gruninger, [email protected]
Kinds of Ontology Languages
Terms
General Logic
Thesauri
formalTaxonomies
Frames(Protege)
Data Models(UML, STEP)
Description Logics
(DAML+OIL)
Principled, informal
hierarchies
ad hoc Hierarchies
(Yahoo!)structured Glossaries
XML DTDs
Data Dictionaries
(EDI)
‘ordinary’Glossaries
XML Schema
DB Schema
Glossaries & Data Dictionaries
MetaData,XML Schemas, & Data Models
Formal Ontologies & Inference
Thesauri, Taxonomies
Michael Gruninger, [email protected]
Kinds of Ontology Languages
Terms
General Logic
Thesauri
formalTaxonomies
FramesProtege
Data Models(UML, STEP)
Description Logics
(DAML+OIL)
Principled, informal
hierarchies
ad hoc Hierarchies
(Yahoo!)structured Glossaries
XML DTDs
Data Dictionaries
(EDI)
‘ordinary’Glossaries
XML Schema
DB Schema
Glossaries & Data Dictionaries
MetaData,XML Schemas, & Data Models
Formal Ontologies & Inference
Thesauri, Taxonomies
Michael Gruninger, [email protected]
Kinds of Ontology Languages
Terms
General Logic
Thesauri
formalTaxonomies
FramesProtege
Data Models(UML, STEP)
Description Logics
(DAML+OIL)
Principled, informal
hierarchies
ad hoc Hierarchies
(Yahoo!)structured Glossaries
XML DTDs
Data Dictionaries
(EDI)
‘ordinary’Glossaries
XML Schema
DB Schema
Glossaries & Data Dictionaries
MetaData,XML Schemas, & Data Models
Formal Ontologies & Inference
Thesauri, Taxonomies
Michael Gruninger, [email protected]
Why do we need formulate ontologies in very expressive languages?
Why do we need formulate ontologies in expressive languages?
It is the only way to produce good ontologies!!
GoodOntology
Kinds of Ontology Languages
General Logic
Description Logics
(DAML+OIL)
Tradeoff betweenexpressive power and computability
How well canwe specify intendedmeaning
What can we compute automatically
Kinds of Ontology Languages
General Logic
Description Logics
(DAML+OIL)
Tradeoff betweenexpressive power and computability
How well canwe specify intendedmeaning
What can we compute automatically
Kinds of Ontology Languages
General Logic
Description Logics
(DAML+OIL)
Tradeoff betweenexpressive power and computability
How well canwe specify intendedmeaning
What can we compute automatically
We need BOTH kinds of languages
General Logic
Description Logics
(DAML+OIL)
Tradeoff betweenexpressive power and computability
How well canwe specify intendedmeaning
What can we compute automatically
Ontologies
Top Level Ontologies for arbitrary domains
• Endurant vs. perdurant (process)
• Parthood• Constitution
Ontologies
Top Level Ontologies for arbitrary domains
• Parthood• Containment• Constitution
Computational ontologies and for specific domains
• GALEN• FMA• SNOMED
Ontologies
Top Level Ontologies for arbitrary domains
• Parthood• Containment• Constitution
Computational ontologies and for specific domains
• GALEN• FMA• SNOMED
Focus on RELATIONS andproperties of relations
Focus on Class hierarchies
Ontologies
Top Level Ontologies for arbitrary domains
Computational ontologies and for specific domains
Requires high expressive power
Requires limitedExpressive power
Focus on RELATIONS andproperties of relations
Focus on Class hierarchies
Ontologies
Top Level Ontologies for arbitrary domains
Computational ontologies and for specific domains
Focus on high expressive power
Focus oncomputation
First order logic is the right language
Description logicsare the right tools
Ontologies
Top Level Ontologies for arbitrary domains
Computational ontologies and for specific domains
Alan and Jeremyuse Description Logicsto as tools to specifya top level ontology
Problems with collectives
Skin
The skin (an organ)
Object-like parts
Skin tissue
Skin tissue = collective of cells
Individual cell
Collective of cells/tissue
The organ ‘skin’
Levels of granularity
Individual cell
Collective of cells
The organ ‘skin’Entities of scale X
Entities of Scale Y
Collectives of Entities of scale Y
Level of granularity X
Level of granularity Y
Levels of granularity
Entities of scale X
Entities of Scale Y
Collectives of Entities of scale Y
Level of granularity X
Level of granularity Y
Entities are treatedas individuals
Members of theCollection areNOT treated asindividuals
Levels of granularity
Entities of scale X
Collectives of Entities of scale Y
Level of granularity X
Entities are treatedas individuals
Members of theCollection areNOT treated asindividuals
Collectives must have MANY members•Cell/molecules/atoms/
We are interested in BIG collectives
• In SMALL collectives we can individuate the members.
• Problem:– The sum/union of two BIG collectives IS a BIG
collection– The INTERSECTION of two BIG collectives is
NOT necessarily a BIG collection– Parthood relation between BIG collectives
CANNOT be modeled using the subset/subcollective relation
The INTERSECTION of two BIG collectives is NOT
necessarily a BIG collection
BIG BIG
small
Parthood relation between masses/collectives
• is DIFFERENT from parthood between individual entities
Weak supplementation principle does NOT hold
Weak supplementation principle
x proper-part-of y
Weak supplementation principle
x proper-part-of y (z)(z proper-part-of y AND overlap zx)
Weak supplementation principle
x proper-part-of y (z)(z proper-part-of y AND overlap zx)
Size of z doesNOT matter
Weak supplementation principle for big collectives
x p-mass-part-of y (z)(z p-mass-part-of y AND overlap zx)
BIG collective
BIG collective
small collective
The weak supplementation principle
relation Partial
order
WSP NPO
is-p-part-of yes yes
is-p-mass-of yes no
Contained-in yes noYou cannot make this distinctionin a Description Logic
Ontologies constrain intended meaning
BadOntology
Ontology does not make enough distinctionsDoes NOT constrain meaning well enough
Empty collectives
• Empty collectives do not have grains/members
• ‘Empty collectives are allowed. This is convenient …’ (Rector & Rogers)
This is always a bad justification!!
Empty collectives
• Empty collectives do not have grains/members
• ‘Empty collectives are allowed. This is convenient …’ (Rector & Rogers)
If we allow empty collectives then collectives areABSTRACT entities
Empty collectives are abstract!
• Abstract entities can be parts of concrete entities– Collective-of-blood-cells part-of blood
concreteabstract
Blood cell grain-of Collective-of-blood-cells
abstractconcrete
Empty collectives are abstract!
Blood cell grain-of Collective-of-blood-cells
abstractconcrete
Blood cell part-of Collective-of-blood-cells
abstractconcrete
Empty collectives are abstract!
Blood cell part-of Collective-of-blood-cells
abstractconcrete
Abstract entities are immaterial and immaterial entities cannot have material parts
–E.g., a hole CANNOT have a material part
So how can a blood cell be part of an ABSTRACT collective of blood cells?
Ontologies constrain intended meaning
BadOntology
Collectives are concrete
Collectives are abstract
So how can a blood cell be part of a collective of blood cells?
Give up emptycollectives
Give up that is-grain-ofis a parthood relation
I suggest: Do BOTH!!
Problems with indeterminacy
Grains, collections, and indeterminacy
“Granular parts are parts by way of being members of a collective that is part of the whole and of indeterminate in number: removing one does not (normally) diminish the whole.” (Rector & Rogers)
Grains, collections, and indeterminacy
“Granular parts are parts by way of being members of a collective that is part of the whole and of indeterminate in number: removing one does not (normally) diminish the whole.” (Rector & Rogers)
There are some grains (e.g., cells) and it is indeterminatewhether they are members/parts of some collection.
Parthood, granularity, indeterminacy
Individual cell
Collective of cells
The organ ‘skin’
ConstitutesGross-part-of
is_grain_of
Determinate parthood
INdeterminacy
Individual cell
Collective of cells
is_grain_of INdeterminacy
At a given time tit is indeterminate (vague) whether a cell is member ofa collective
Collectives have differentmembers at different times and it is hardto keep track of thosechanges
Parthood, granularity, indeterminacy
At a given time tit is indeterminatewhether a cell ismember of the collective
Collectives have differentmembers at different times and it is hardto keep track of thosechanges
Only true for some cells•At the boundary of the skin•For most cells it is pretty clear whether they are parts of a collective
This is neither indeterminacy norvagueness
Parthood, granularity, indeterminacy
Time-indexed is_grain_of
So, what does indeterminacy mean???
BadOntology
Ontology does not make enough distinctionsDoes NOT constrain meaning well enough
More problems with indeterminacy
Individual cell
Collective of cells
The organ ‘skin’
Constitutes(determinate)
is_grain_of(indeterminate)
Collective of cells
The organ ‘skin’
Individual cell
Part-ofimplies
Part-ofimplies
Determinate??????????
INdeterminate??????????
Problems with transitivity
Problems with transitivity
Two questions are confused
Does the relation Xhave the property Y?
e.g., is parthood transitive
Can we exploit the fact thatrelation X has property Yfor reasoning purposes
e.g., can we exploit transitivityfor reasoning
Ontology Knowledge rep. & reasoning
What does it mean ‘relation R is transitive’
For ALL x,y,z [IF R(x,y) AND R(y,z) THEN R(x,z)]
IF is_grain_of(x,y) AND is_grain_of(y,z) THEN is_grain_of(x,z)
If this formula is true in the bio-medical domain then is_grain_of is transitive in this domain
Is is_grain_of transitive ?
IF is_grain_of(x,y) AND is_grain_of(y,z) THEN is_grain_of(x,z)
The premise is false or The conclusionis true
is_grain_of(x,y)
individual collective
is_grain_of(y, z)
is_grain_of(x,y) AND is_grain_of(y,z)
is_grain_of is (trivially) transitive
Problems with transitivity
Two questions are confused
Does the relation Xhave the property Y?
e.g., is grain_of transitive
Can we exploit the fact thatrelation X has property Yfor reasoning purposes
e.g., can we exploit transitivityfor reasoning
Formal Ontology Computational ontologies
Conclusions
The short version• It is WRONG to consider description logics
as tools for formal ontology, i.e., as formal languages in order to represent top-level ontologies
• DLs are VERY valuable and capable tools for computational ontologies that support reasoning
The short version• It is WRONG to consider description logics
as tools for formal ontology, i.e., as formal languages in order to represent top-level ontologies
• DLs are VERY valuable and capable tools for computational ontologies that support reasoning
• Computational ontologies should be derived (built in compliance with) a formal ontology in First Order Logic
The GOOD and the BAD
• Using SEP-triples in a formal (top-level) ontology IS BAD
• Using SEP-triples in a computational ontology to provide computationally efficient transitivity reasoning is GOOD (assuming that you have an underlying formal ontology that tells you what you are reasoning about)
The GOOD and the BAD
• To ignore properties that you cannot express in your language is BAD in a formal ontology
• To ignore properties that you cannot express in your (computable) language is all one can do in a computational ontology
Ontologies for biomedicine
• Formal top-level ontology expressed in first order logic
Ontologies for biomedicine
• Formal top-level ontology expressed in first order logic
• Computational ontologies in DLs based on a formal ontology