This document summarizes 21 years of work in applied ontology from 1993 to 2014 at the Laboratory for Applied Ontology (LOA). It discusses how applied ontology builds on philosophy, cognitive science, linguistics and logic to understand people's assumptions about the world. It emphasizes the importance of ontological analysis of content before representation. Formal ontologies allow precise representations of domain knowledge and help address problems like distinguishing between different types of concepts and relationships between concepts. The document outlines the role of formal ontology in providing a common vocabulary and addressing key challenges in knowledge representation.

1. Twentyone Years of Applied Ontology
1993-2014
Nicola Guarino
National Research Council, Institute for Cognitive Science and
Technologies (ISTC-CNR)
Laboratory for Applied Ontology (LOA)
www.loa.istc.cnr.it
!
Thanks to Giancarlo Guizzardi and all the LOA people!

2. The most cited paper in
applied ontology
Presented at International Workshop on Formal Ontology, March 1993,
Padua, Italy – Organized by LADSEB-CNR (N. Guarino and R. Poli)

3. Carving the reality at its joints:
good ontologists
like good butchers [Plato]
3

4. …but, despite reality resists, still many possibilities are open!
4

5. 4
Applied Ontology:
an emerging interdisciplinary area
• Applied Ontology builds on philosophy, cognitive science, linguistics
and logic with the purpose of understanding, clarifying, making explicit
and communicating people's assumptions about the nature and
structure of the world.!
• This orientation towards helping people understanding each other
distinguishes applied ontology from philosophical ontology, and
motivates its unavoidable interdisciplinary nature.
ontological analysis: study of !
content (of these assumptions) as such !
(independently of their representation)

6. Focusing on content

7. 15
Do we know what to REpresent?
• First analysis,!
• THEN representation…!
Unfortunately, this is not the current practice…!
• Computer scientists have focused on the structure of
representations and the nature of reasoning more than on
the content of such representations!
Essential ontological promiscuity of AI: any agent creates its
own ontology based on its usefulness for the task at hand
(Genesereth and Nilsson 1987)
No representation without
ontological analysis!

8. Logic is neutral about content
...but very useful to describe the formal structure (i.e.,
the invariances) of content

9. 9
Kinds of knowledge"
(Carnap - Meaning and Necessity)
logical Fido is black!
synthetic
either Fido is black or Fido is not black!
If Jack is a bachelor, then he is not married
analytic
terminological
(assertional)
Terminological knowledge is about
relationships between terms and concepts

10. 7
The problem: subtle distinctions in meaning
!
The e-commerce case:!
!
“Trying to engage with too many partners too fast is one of the main reasons
that so many online market makers have foundered”
The transactions they had viewed as simple and routine
actually involved many
subtle distinctions in terminology and meaning”!
!
Harvard Business Review, October 2001

11. 9
Subtle distinctions in meaning...
• What is an application to a public administration?!
• What is a service?!
• What is a working place?!
• What is an unemployed person?!
• What is a customer?!
• What is an organization?!
• What is a contract?
The key problems!
• content-based information access (semantic matching)!
• content-based information integration (semantic integration)

12. Semantic Interoperability is considered to be
the problem of this decade…[currently]
costing productivity, lives and billions of
dollars annually…the overall human and
financial cost to society from our failure to
share and reuse information is many times the
cost of the systems’ operation and
maintenance [OMG, SIMF]

13. When subtle distinctions are important:!
fine prints
An ontology is like a contract's fine print, one of
those things which require a very precise technical
jargon, which you might ignore in many cases, but
which can save your business in critical situations.
13

16. Product Classification
• U.S. Product Classification:
• Dolls: Representing Only a Human-Being (12%)
• Toys: Anything that does not represent only a
human being (6%)

18. What is an ontology

19. 19
Philosophical ontologies
• Ontology: the philosophical discipline!
!
• Study of what there is (being qua being...)!
...a liberal reinterpretation for computer science: !
!
content qua content, independently of the way it is represented!
!
• Study of the nature and structure of “reality”!
!
• A (philosophical) ontology: a structured system of entities assumed
to exists, organized in categories and relations

20. Computational ontologies
20
Specific (theoretical or computational) artifacts
expressing the intended meaning of a vocabulary
in terms of primitive categories and relations describing
the nature and structure of a domain of discourse
Gruber: “Explicit and formal specifications of a conceptualization”
Computational ontologies, in the way they evolved, unavoidably mix
together philosophical, cognitive, and linguistic aspects.!
Ignoring this intrinsic interdisciplinary nature
makes them almost useless.

21. Perception Reality
Bad
Ontology
~Good
Ontology
Conceptualization
Language L
Intended
models for
each IK(L)
Ontological commitment K
(selects D’⊂D and ℜ’⊂ℜ)
Interpretations
I
Models MD’(L)
Ontology models
relevant invariants within
and across presentation
patterns:
D, ℜ
State of
aSfftaaitres of
Parfefsaeinrtsation
patterns
Phenomena

22. Less good
WORSE
22
Ontology Quality: Precision and Correctness
Low precision, max correctness
Low precision, low correctness
Good
High precision, max correctness
BAD
Max precision, low correctness

23. Why ontological precision is important

24. Database A: keeping track of fruit stock
24
Variety Quantity
Granny Smith 12
Golden delicious 10
Stark delicious 15

25. Database B: keeping track of juice stock
25
Variety Quantity
Granny Smith 12
Golden delicious 10
Stark delicious 15

26. Why ontological precision is important
26
All
interpretations
of “apple”
Area
of false
agreement!
B - Juice
producer’s
intended
interpretations
A - Apple
producer’s
intended
interepretations
Interpretations
allowed by B’s
ontology
Interpretations
allowed by A’s
ontology

27. When is a precise (and accurate) ontology useful?
27
1. When subtle distinctions are important!
2. When recognizing disagreement is important!
3. When careful explanation and justification of ontological commitment
is important!
4. When mutual understanding is more important than interoperability.

28. Two classic KR problems
solved by formal ontology

29. 29
Problem 1 - Terminological competence
How many rock kinds are there?
rock
igneous rock sedimentary rock
metamorphic rock
large rock grey rock
grey
sedimentary
rock
large grey igneous rock
pet metamorphic rock
[From Brachman, R ., R. F ikes, et al. 1983. “Krypton: A Functional Approach to
Knowledge Representation”, IEEE Computer]

30. 30
The answer
• According to Brachman & Fikes 83:
• It’s a dangerous question, only “safe” queries about analytical
relationships between terms should be asked
• In a previous paper by Brachman and Levesque on terminological
competence in knowledge representation [AAAI 82]:
• “an enhancement mode transistor (which is a kind of
transistor) should be understood as different from a pass
transistor (which is a role a transistor plays in a larger circuit)”
• These issues have been simply given up while striving for logical
simplification and computational tractability
• The OntoClean methodology, based on formal ontological
analysis, allows us to conclude: there are 3 kinds of rocks

31. 31
Problem 2 - What’s in a link?
• Woods’ “What’s in a link?” (1975):
JOHN
HEIGHT: 6 FEET
KISSED: MARY
!
• "no longer do the link names stand for attributes of a node,
but rather arbitrary relations between the node and other
nodes”
• different notations should be used

32. Structured concepts: a broader picture
JOHN
HEIGHT: 6 FEET
RIGHT-LEG: BROKEN
MOTHER: JANE
KISSED: MARY
JOB: RESEARCHER
intrinsic quality
part
role
external relation
relational quality
We need different primitives to express different structuring relationships among concepts
We need to represent non-structuring relationships separately
Current description logics tend to collapse EVERYTHING!
32

33. The Ontological Level
• Guarino N. 1994. The Ontological Level. In R. Casati, B. Smith and G. White (eds.), Philosophy
and the Cognitive Sciences (by 16th International Wittgenstein Symposium, Kirchberg am
Wechsel, Austria, 1993). Vienna, Hölder-Pichler-Tempsky 1994!
• Guarino, N. 2009. The Ontological Level: Revisiting 30 Years of Knowledge Representation. In
Alex Borgida, Vinay Chaudhri, Paolo Giorgini, Eric Yu (eds.), Conceptual Modelling: Foundations
and Applications. Essays in Honor of John Mylopoulos, Springer Verlag 2009
Level Primitives Interpretation Main feature
Logical Predicates,
functions
Arbitrary Formalization
Epistemological Structuring
relations
Arbitrary Structure
Ontological Ontological
relations
Constrained
(meaning postulate s )
Meaning
Conceptual Conceptual
relations
Subjective Conceptualization
Linguistic Linguistic
terms
Subjective Language
dependence

34. 34
The formal tools of ontological analysis
• Theory of Parts (Mereology) !
• Theory of Unity and Plurality!
• Theory of Essence and Identity!
• Theory of Dependence!
• Theory of Composition and Constitution!
• Theory of Properties and Qualities
The basis for a common ontology
vocabulary
Idea of Chris Welty, IBM Watson Research
Centre, while visiting our lab in 2000

35. 35
The semantic web architecture [Tim Berners Lee 2000]

36. The Semantic Web Architecture (revised)
36

37. 37
Formal Ontology
• Theory of formal distinctions and connections within:!
• entities of the world, as we perceive it (particulars)!
• categories we use to talk about such entities (universals)!
• Why formal?!
• Two meanings: rigorous and general!
• Formal logic: connections between truths - neutral wrt truth!
• Formal ontology: connections between things - neutral wrt reality!
• NOTE: “represented in a formal language” is not enough for
being formal in the above sense!!
• Analytic ontology may be a better term to avoid this confusion

38. Knowledge representation, conceptual
modeling, and ontological analysis
• Data models encode knowledge about the world in order to easily and
efficiently access it!
• Conceptual models describe some aspects of the world for the
purpose of understanding and communication!
• Knowledge representations encode knowledge about the world in
order to easily and efficiently access it and use it to generate new
knowledge!
• Computational ontologies characterize the language used to talk
about the world in order to reduce ambiguities and misunderstandings
• Ontological analysis: systematic way to understand and make explicit
the world assumptions behind a certain description:!
• How do we believe the world is, when we say !
• This rose is red!
• John is married with Mary!
• John is a student!
• My name is Nicola
38
different role of axioms
possible expansions of domain !
and vocabulary

39. Ontological analysis as a detective lens
• Most of our true statements about the world
are approximate!
• What makes them true?!
• Where…?!
• When…?!
• Who…?!
• Why…?!
• Ontological analysis as a search for Truth-makers
39

40. A bit of history - Community building initiatives
• 1993: 1st Int. workshop on Formal Ontology & Information Systems!
• 1998: 1st FOIS conference!
• 2002: Ontolog forum!
• 2005: Applied Ontology (IOS Press)!
• 2005: ECOR, NCOR, JCOR...!
• 2006: First public discussion on an ontology association at FOIS !
• 2008: Public assembly at FOIS (Saarbrucken)!
• 2011: Applied Ontology gets official ISI recognition!
• 2009-2012: Several focused conferences (FOMI, WOMO...)!
• 2012: IAOA permanent co-organizer of Ontology Summit!
• 2013: !
• IJCAI invites authors of best FOIS papers; !
• Philosophical community (J. Lowe) organizes a conference on applied
ontology!
!
• In parallel: various consortia focusing mainly on Semantic Web
40

41. A new discipline is emerging

42. A hint from the past:
The emergence of psychology as a science
• 1874: Franz Brentano publishes Psychology from an Empirical
Standpoint!
• 1879: Wilhelm Wundt establishes the world’s first psychological
laboratory at the University of Leipzig!
• 1883: First psychology lab in America established at Johns Hopkins!
• University authorities give Wundt's Leipzig laboratory formal recognition!
• 1889: First international congress of Psychology!
• Alexius Meinong founds Laboratory of Psychology in University of Graz!
• First Chinese translation of a Western psychology book!
• 1892: American Psycgological Association founded (42 members)!
• 1894: Stumpf called to serve as professor of philosophy in Berlin with
the explicit task of establishing there an institute of psychology
42

43. Typical reactions to the founding of
a new discipline
If one is interested in the relations between
fields which, according to customary academic
divisions, belong to different departments, then
he will not be welcomed as a builder of bridges,
as he might have expected, but will rather be
regarded by both sides as an outsider and
troublesome intruder.
! R. Carnap, "My Work in Philosophy Begins".
18

44. 20
From metaphysics to ontology
as a science
Metaphysics (phil.)!
! The science of being!
Ontology (phil.) !
! A theory of the types of entities existing in reality, and of the relations
between these types!
Ontologies (tech.)!
! Standardized classification systems which enable data from different
sources to be combined!
Ontology (science)!
! The science which develops theories of the types of entities existing in
(people’s assumptions about) given domains of reality, and of the relations
between these types including: ways of testing such theories, ways of
using such theories, e.g. in supporting reasoning about empirical data
collected by other sciences

45. The Association is addressed to:
•
Philosophers who have an interest in applying their analytical tools to
technology advancement;
•
cognitive scientists, linguists and terminologists aware of the subtle
interplays among ontology, language, and cognition;
•
computer scientists and IT professionals aware of the desperate need of
a sound interdisciplinary approach for building future generation socio-technical
systems.

46. From the Statute
“The Association is a non-profit organization the purpose of which is to
promote interdisciplinary research and international collaboration at
the intersection of philosophical ontology, linguistics, logic,
cognitive science, and computer science, as well as in the
applications of ontological analysis to conceptual modeling,
knowledge engineering, knowledge management, information-systems
development, library and information science, scientific
research, and semantic technologies in general.”
5

47. IAOA: a unique combination of key aspects
1. Interdisciplinarity!
2. Cooperation between academy, industry, and communities of
practice (with an eye on education)!
3. Scientific authoritativeness!
4. Openness!
5. Legal status!
6. Transparent governance

48. The IAOA flagship journal: Applied Ontology
Editors in chief:
Nicola Guarino
ISTC-CNR
Mark Musen
Stanford University
!
!
IOS Press
Amsterdam, Berlin, Washington,
Tokyo, Beijing
www.applied-ontology.org
Now indexed by ISI and Scopus.
Impact Factor: 1.105

49. 49

50. Is applied ontology successful?

53. “The
fast-­‐growing
science
of
ontology
could
exert
a
greater
impact
on
humanity
than
the
rise
of
the
Internet…ontologies
allow
for
data
to
interoperate
and
for
machines
to
make
inferences.
The
report
calls
for
more
education
and
training
opportunities
for
ontologists,
and
for
better
means
of
connecting
ontologists
with
organizations
that
need
them.

56. In 2014, the Financial Research Advisory
Committee (FRAC) of the US Treasury’s Office of
Financial Research (OFR) unanimously
recommended the adoption of ontology as a
significant part of the OFR’s initiatives to meet the
requirements put forth by the Dodd-Frank Act

57. DataModelingGuide(DMG)ForAnEnterpriseLogicalDataModel,V2.3;15March2011
Data Modeling Guide (DMG)
For An Enterprise
Logical Data Model (ELDM)
Version 2.3
March 15, 2011
The U.S. Government has rights in this document in accordance with DFAR
252.227-7013 Rights in Technical Data – Noncommercial Item (Nov 1995)
under contract number H98230-09-C-1180.

59. Ontologies and (big) data

61. A new discipline (or science) is emerging?
Maybe.!
See the history of Psychology, Systems Engineering...!
See recent proposals for Web Science, Services Science, Data
Science??…!
For sure, a humble, truly interdisciplinary approach is needed,
focusing on letting new ideas, approaches, methodologies emerge
from the mutual cross-fertilization of different disciplines.
3

62. Current Research topics Methodology
Ontology of socio-technical systems!
• Multi-agent systems, social interaction, and collective intentionality!
• Ontology of organizations and social roles!
• Ontology of functions, artefacts, and engineering design!
• Integrated modelling of organizations, processes, and services!
• Ontological foundations of service science and value-cocreation!
• Visual recognition of crisis situations; role of emotions in crisis situations!
• Role of crises and contradiction in social interaction!
Ontology, language, cognition!
• Ontology, cognition, and natural language semantics!
• Ontology and lexical resources!
• Formal semantics of discourse and dialogue relations!
• Ontology and epistemology of measurement!
• Perception of visual objects!
• Perception, social conventions, and ontological constructivism!
Principles and methodologies for ontological analysis, conceptual modeling,
knowledge representation, and software engineering!
• Formal ontological analysis: theories of properties, qualities, parts, unity,
identity, dependence...!
• Ontology-driven conceptual modelling