![Page 1: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/1.jpg)
Ontology-Driven Conceptual Modeling
Chris WeltyIBM Watson Research Center
![Page 2: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/2.jpg)
2
Acknowledgements
People
Nicola GuarinoCladio MasoloAldo GangemiAlessandro Oltramari
Bill Andersen
OrganizationsVassar College, USA
LOA-CNR, Trento
OntologyWorks, Inc.
![Page 3: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/3.jpg)
3
Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
![Page 4: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/4.jpg)
4
What is Ontology?
• A discipline of Philosophy– Meta-physics dates back to Artistotle
• Meta (after) + physica (physical, real)– Ontology dates back to 17th century
• Ontos (that which exists) + logos (knowledge of)• As in TorONTO, ONTario, ON TOp
– The science of what is (in the universe)– “One universe, One ontology”
• Quine, 1969:“To exist is to be the value of a quantified variable”
![Page 5: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/5.jpg)
5
What is Ontology?
• Borrowed by AI community– McCarthy (1980) calls for “a list of things that
exist”– Specify all the kinds of things that can be the
values of variables• Evolution of meaning in CS
– Now refers to domain modeling, conceptual modeling, knowledge engineering, etc.
• Note: not a “new name for an old thing”
![Page 6: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/6.jpg)
6
What is an Ontology?
• Poor definition:“Specification of a conceptualization” [Gruber, 1993]
• Better:“Description of the kinds of entities there are and
how they are related.”• Good ontologies should provide:
– Meaning – Organization – Taxonomy
– Agreement– Common Understanding– Vocabulary– Connection to the “real world”
![Page 7: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/7.jpg)
7
What is an Ontology?
complexity
a catalog
a set of generallogicalaxioms
a glossary
a set of termsa thesaurus
a collection of
taxonomies
a collection of frames
with automated reasoningwithout automated reasoning
![Page 8: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/8.jpg)
8
What is an Ontology?
complexity
a catalog
a set of generallogicalaxioms
a glossary
a set of termsa thesaurus
a collection of
taxonomies
a collection of frames
with automated reasoningwithout automated reasoning
![Page 9: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/9.jpg)
9
Key Challenges• Must build/design, analyze/evaluate, maintain/extend,
and integrate/reconcile ontologies
• Little guidance on how to do this– In spite of the pursuit of many syntactic standards– Where do we start when building an ontology?– What criteria do we use to evaluate ontologies?– How are ontologies extended?– How are different ontological choices reconciled?
• Ontological Modeling and Analysis– Does your model mean what you intend?– Will it produce the right results?
![Page 10: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/10.jpg)
10
Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
![Page 11: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/11.jpg)
11
Motivation
Provide a sound basis for analyzing ontological decisions
“If you can give me a way to shorten the length of the arguments I have with these doctors, you have made a significant
contribution…”-Alan Rector
![Page 12: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/12.jpg)
12
Most ontology efforts fail
• Why?– The quality of the ontology dictates its impact– Poor ontology, poor results– Ontologies are built by people
…The average IQ is 100
![Page 13: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/13.jpg)
13
Which one is better?
T-Series
ThinkPad
T Series
ThinkPad Model
Thinkpad
model
![Page 14: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/14.jpg)
14
Which one is better?
Computer
has-part
MemoryDisk Drive
Computer Part
Memory PartDisk Part
Computer Part
Disk Drive Memory
Computer
has-part
Due to: Guizzardi, et al, 2004.
![Page 15: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/15.jpg)
15
• Methodology to help analyze & build consistent ontologies– Formal foundation of ontological analysis– Meta-properties for analysis– “Upper Level” distinctions
• Standard set of upper-level concepts• Standardizing semantics of ontological relations
• Common ontological modeling pitfalls– Misuse of intended semantics
• Specific work focused on clarifying the subsumption (is-a, subclass) relation
Contributions
In this presentation, I will use “property” in its logical sense as a unary predicate (i.e. class), NOT
the semantic web sense as a binary predicate (i.e. relation).
![Page 16: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/16.jpg)
16
Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
![Page 17: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/17.jpg)
17
Approach
• Draw fundamental notions from Philosophy• Establish a set of useful meta-properties, based
on behavior wrt above notions • Explore the way these meta-properties combine
to form relevant property kinds• Explore the constraints imposed by these
property kinds.
![Page 18: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/18.jpg)
18
Basic Philosophical Notions(taken from Formal Ontology)
• Identity– How are instances of a class distinguished from each
other• Unity
– How are all the parts of an instance isolated• Essence
– Can a property change over time• Dependence
– Can an entity exist without some others• Permanence
– How long do entities last
![Page 19: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/19.jpg)
19
Essence and Rigidity
• Certain entities have essential properties.– Pillows must be soft.– John must be a person.
• Certain properties are essential to all their instances (compare being a person with being soft).
• These properties are rigid - if an entity is ever an instance of a rigid property, it must always be.
![Page 20: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/20.jpg)
20
Formal Rigidity is rigid (+R): x (x) � (x)
– e.g. Person, Apple
is non-rigid (-R): x (x) ¬ � (x)– e.g. Red, Male
is anti-rigid (~R): x (x) ¬ � (x)– e.g. Student, Agent
![Page 21: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/21.jpg)
21
Identity and Unity
• Identity: is this my dog?
• Unity: is the collar part of my dog?
![Page 22: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/22.jpg)
22
Identity criteria
• Classical formulation:
(x) (y) ((x,y) x = y)
• Generalization:(x,t) (y,t’) ((x,y,t,t’) x = y)
(synchronic: t = t’ ; diachronic: t ≠ t’)
• In most cases, is based on the sameness of certain characteristic features:
(x,y, t ,t’) = z ((x,z,t) (y,z,t’))
![Page 23: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/23.jpg)
23
A Stronger Notion:Global ICs
• Local IC:
(x,t) (y,t’) ((x,y,t,t’) x = y)
• Global IC (rigid properties only):
(x,t) ((y,t’) (x,y,t,t’) x = y)
![Page 24: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/24.jpg)
24
Identity meta-properties
• Supplying (global) identity (+O)– Having some “own” IC that doesn’t hold for a
subsuming property
• Carrying (global) identity (+I)– Having an IC (either own or inherited)
• Not carrying (global) identity (-I)
![Page 25: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/25.jpg)
25
Unity Criteria
• An object x is a whole under iff is an equivalence relation that binds together all the parts of x, such that
P(y,x) (P(z,x) y,z))but not
y,z) x(P(y,x) P(z,x))
• P is the part-of relation can be seen as a generalized indirect connection
![Page 26: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/26.jpg)
26
Unity Meta-Properties
• If all instances of a property are wholes under the same relationcarries unity (+U)
• When at least one instance of is not a whole, or when two instances of are wholes under different relations, does not carry unity (-U)
• When no instance of is a whole, carries anti-unity (~U)
![Page 27: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/27.jpg)
27
Property Dependence
• Does a property holding for x depend on something else besides x? (property dependence) – P(x) y Q(y)– y should not be a part of x
• Example: Student/Teacher, customer/vendor
![Page 28: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/28.jpg)
28
Permanence
Dead PersonLiving Person
Person
Chris Aristotle
Person
Chris Aristotle
Person
Chris Aristotle
![Page 29: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/29.jpg)
29
Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
![Page 30: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/30.jpg)
30
“Upper Level” Ontology
• The “media independent” knowledge– Fundamental truths of the universe– Non contextual (aka formal)
• Is there only one?• Upper level Large• Proven value
– A place to start– Semantic integration
![Page 31: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/31.jpg)
31
Upper LevelWhere do I start?
• Particulars– Concrete
• Location, event, object, substance, …– Abstract
• information, story, collection, …
• Universals– Property (Class)– Relation
• Subsumption (subclass), instantiation, constitution, composition (part)
![Page 32: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/32.jpg)
32
A formal ontology of properties
Property
Non-sortal-I
Role~R+D
Sortal+I
Formal Role
Attribution -R-D
Category +R
Mixin -D
Type +O
Quasi-type -O
Non-rigid-R
Rigid+R
Material roleAnti-rigid~R Phased sortal -D +L
![Page 33: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/33.jpg)
33
Sortals, categories, and other properties
• Sortals (horse, triangle, amount of matter, person, student...)– Carry identity– Usually correspond to nouns– High organizational utility– Main subclasses: types and roles
• Categories (universal, particular, event, substance...)– No identity– Useful generalizations for sortals– Characterized by a set of (only necessary) formal properties– Good organizational utility
• Other non-sortals (red, big, decomposable, eatable, dependent, singular...)– No identity– Correspond to adjectives– Span across different sortals– Limited organizational utility (but high semantic value)
![Page 34: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/34.jpg)
34
Formal Ontology of Relations
• Subsumption• Instantiation• Part/Whole• Constitution• Spatial (Cohn)• Temporal (Allen)
![Page 35: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/35.jpg)
35
Subsumption• The most pervasive relationship in ontologies
– Influence of taxonomies and OO
• AKA: Is-a, a-kind-of, specialization-of, subclass (Brachman, 1983)– “horse is a mammal”
• Capitalizes on general knowledge– Helps deal with complexity, structure– Reduces requirement to acquire and represent redundant specifics
• What does it mean?
� x (x) (x)
Every instance of the subclass is necessarily an instance of the superclass
![Page 36: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/36.jpg)
36
The Backbone Taxonomy
Assumption: no entity without identityQuine, 1969
• Since identity is supplied by types, every entity must instantiate a type
• The taxonomy of types spans the whole domain• Together with categories, types form the backbone
taxonomy, which represents the invariant structure of a domain (rigid properties spanning the whole domain)
![Page 37: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/37.jpg)
37
Rigidity Constraint
+R ~R
• Why?
� x P(x) Q(x)
Q~R
P+R
O10
![Page 38: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/38.jpg)
38
Identity Conditions along Taxonomies
• Adding ICs:– Polygon: same edges, same angles
• Triangle: two edges, one angle– Equilateral triangle: one edge
• Just inheriting ICs:– Person
• Student
![Page 39: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/39.jpg)
39
Identity Disjointness Constraint
Properties with incompatible ICs are disjoint
Besides being used for recognizing sortals, ICs impose constraints on them, making their ontological nature explicit:
Examples:• sets vs. ordered sets• amounts of matter vs. assemblies
![Page 40: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/40.jpg)
40
Unity Disjointness Constraint
Properties with incompatible UCs are disjoint+U ~U
![Page 41: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/41.jpg)
41
Taxonomic Constraints
• +R ~R• -I +I• -U +U• +U ~U• -D +D
• Incompatible IC’s are disjoint
• Incompatible UC’s are disjoint
• Categories subsume everything
• Roles can’t subsume types
![Page 42: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/42.jpg)
42
Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
![Page 43: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/43.jpg)
43
Overloading Subsumption Common modeling pitfalls
• Instantiation• Constitution• Composition• Disjunction• Polysemy
![Page 44: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/44.jpg)
44
Instantiation (1)
T21
My ThinkPad (s# xx123)
ThinkPad Model
Ooops…
Question: What ThinkPad models do you sell?Answer should NOT include My ThinkPad -- nor yours.
Does this ontology mean that My ThinkPad is a ThinkPad Model?
![Page 45: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/45.jpg)
45
Instantiation (2)
T Series
My ThinkPad (s# xx123)
ThinkPad ModelNotebook Computer
model T 21
![Page 46: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/46.jpg)
46
Composition (1)
MemoryDisk Drive
Computer
Question: What Computers do you sell?Answer should NOT include Disk Drives or Memory.
Micro Drive
![Page 47: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/47.jpg)
47
Composition (2)
MemoryDisk Drive
Computer
Micro Drive
part-of
![Page 48: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/48.jpg)
48
Disjunction (1)
MemoryDisk Drive
Computer
Micro Drive
has-partComputer Part
Flashcard-110Camera-15has-part
Unintended model: flashcard-110 is a computer-part
![Page 49: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/49.jpg)
49
Disjunction (2)
Computerhas-part
Disk Drive Memory …
![Page 50: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/50.jpg)
50
Polysemy (1)(Mikrokosmos)
Abstract EntityPhysical Object
Book
Question: How many books do you have on Hemingway?Answer: 5,000
…..
![Page 51: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/51.jpg)
51
Polysemy (2)(WordNet)
Abstract EntityPhysical Object
BookSense 1
BookSense 2
….. Biography of Hemingway
![Page 52: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/52.jpg)
52
Constitution (1)(WordNet)
Amount of Matter
Physical Object
Entity
ComputerClayMetal
Question: What types of matter will conduct electricity?Answer should NOT include computers.
![Page 53: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/53.jpg)
53
Constitution (2)
Amount of Matter Physical Object
Entity
ComputerClayMetal
constituted
![Page 54: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/54.jpg)
54
Technical Conclusions• Subsumption is an overloaded relation
– Influence of OO – Force fit of simple taxonomic structures– Leads to misuse of is-a semantics
• Ontological Analysis– A collection of well-defined knowledge structuring relations– Methodology for their consistent application
• Meta-Properties for ontological relations• Provide basis for disciplined ontological analysis
![Page 55: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/55.jpg)
55
Applications of Methodology
• Ontologyworks• IBM• Ontoweb• TICCA, WedODE, Galen, …• Strong interest from and participation in
– Semantic web (w3c)– IEEE SUO– Wordnet– Lexical resources
![Page 56: Ontology-Driven Conceptual Modeling Chris Welty IBM Watson Research Center](https://reader036.vdocuments.us/reader036/viewer/2022062318/551499ce550346f06e8b56c1/html5/thumbnails/56.jpg)
56
References
• Guarino, Nicola and Chris Welty. 2002. CACM. 45(2):61-65.• Smith, Barry and Chris Welty. 2001. Ontology: Towards a new
synthesis. In Formal Ontology in Information Systems. ACM Press.
• Welty, Chris and Nicola Guarino. 2001. In J. Data and Knowledge Engineering. 39(1):51-74. October, 2001.
• Guarino, Nicola and Chris Welty. 2000. In Proceedings of ER-2000: The 19th International Conference on Conceptual Modeling.
• Guarino, Nicola and Chris Welty. 2000. In Proceedings of ECAI-2000: The European Conference on Artificial Intelligence.
• Guizzardi, G.; Wagner, G.; Guarino, N.; van Sinderen, M. 2004. In Proceedings of 16th CAiSE.