short introduction in ontology engineering at the Semantic Computing Summer School in Berkeley, 2009.

1. Elena Simperl @SSSC2009, Berkeley, CA.

2. Fundamentals of ontologies
F d l f l i
Definitions, features, classifications, applications,
modelling principles
Ontology engineering
Methodologies, methods
Methodologies methods and tools to build
build,
manage and use ontologies
*Thanks to Tobias Bürger, Asuncion Gomez Perez and Martin
Hepp for providing valuable contents to this lecture

4. An ontology defines the basic terms and relations
A t l d fi th b i t d l ti
comprising the vocabulary of a topic area, as well as the
rules for combining terms and relations to define
extensions to the vocabulary
t i t th b l
Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling
Technology for Knowledge Sharing. AI Magazine. Winter 1991. 36‐56
An ontology is an explicit specification of a
conceptualization
Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition.
Vol. 5. 1993. 199‐220
Vol 5 1993 199‐220

5. An ontology is a hierarchically structured set of terms for
A t l i hi hi ll t t d t f t f
describing a domain that can be used as a skeletal foundation
for a knowledge base
B. Swartout; R. Patil; k. Knight; T. Russ. Toward
B Swartout R Patil k Knight T Russ Toward Distributed Use of Large Scale Ontologies
Use of Large‐Scale
Ontological Engineering. AAAI‐97 Spring Symposium Series. 1997. 138‐148
An ontology provides the means for describing explicitly the
conceptualization behind the knowledge represented in a
knowledge base
A. Bernaras;I. Laresgoiti; J. Correra. Building and Reusing Ontologies for Electrical Network
Applications ECAI96. 12th European conference on Artificial Intelligence. Ed. John Wiley
& Sons, Ltd. 298‐302
& S Ltd 8

6. “An ontology is a formal, explicit specification of a shared conceptualization”
Consensual
Knowledge
Machine-readable
Concepts, properties
relations, functions, Abstract model and
constraints, axioms, simplified view of some
are explicitly defined phenomenon in the world
that we want to represent
Studer, Benjamins, Fensel. Knowledge Engineering: Principles and Methods. Data and Knowledge Engineering. 25 (1998) 161-197
161 197

7. SUMO

8. Modelled knowledge about a specific domain
Defines
A common vocabulary
The meaning of terms
How terms are interrelated
Consists of
Conceptualization and implementation
Contains
Ontological primitives

9. Concepts are organized in taxonomies
C i d i i
Relations
• R: C1 x C2 x ... x Cn-1 x Cn
Functions
• F: C1 x C2 x ... x Cn-1 --> Cn
Instances
• Elements
Axioms
• Sentences which are always true

10. Issue of the conceptualization
Upper‐level/Top‐level
Core
Domain
Task
Application
Representation
p
Degree of formality
Highly informal: in natural language
Semi‐informal: in a restricted and structured form of natural
S ii f l i i d d d f f l
language
Semi‐formal: in an artificial and formally defined language
Rigorously formal: in a language with formal semantics, theorems
Rigorously formal: in a language with formal semantics theorems
and proofs of such properties as soundness and completeness

11. Thessauri General
Formal Frames Logical
“narrower term”
Catalog/ID is-a (properties) constraints
relation
Terms/
T / Informal
I f l Formal Value
V l Disjointness,
Disjointness
glossary is-a instance Restrs. Inverse, part-Of
...
Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web.
Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001.

12. R Application Domain Application Domain
E Ontology Task Ontology
U
S Domain Ontology Domain Task Ontology
A
Core Ontology Task Ontology
B
I
Top-Level Ontology
L
I
General/Common Ontology
T
Y
Representation Ontology

13. Ontologies can be built using various languages with various
O t l i b b ilt i i l ith i
degrees of formality
Natural language
UML
ER
OWL/RDFS
WSML
FOL
...
The formalism and the language limit the kind of knowledge that
Th f li d th l li it th ki d f k l d th t
can be represented
A domain model is not necessarily a formal ontology only because
it is written in OWL

16. (defconcept Travel (defrelation Pays
"A journey from place to place" :is
:is-primitive (:function (?room ?Discount)
(:and (-
( (Price ?room) (/(*(Price ?room) ?Discount) 100)))
(/( (Price
(:all arrivalDate Date)(:exactly 1 arrivalDate) :domains (Room Number)
(:all departureDate Date)(:exactly 1 :range Number)
departureDate)
(:all companyName String)
(:all singleFare Number)(:at-most singleFare 1))) (defrelation connects
"A road connects two different cities"
:arity 3
:domains (Location Location)
(tellm (AA7462 AA7462-08-Feb-2002) :range R dS ti
RoadSection
(singleFare AA7462-08-Feb-2002 300) :predicate
(departureDate AA7462-08-Feb-2002 Feb8-2002) ((?city1 ?city2 ?road)
(arrivalPlace AA7462-08-Feb-2002 Seattle)) (:not (part-of ?city1 ?city2))
(
(:not (part-of ?city2 ?city1))
(p y y ))
(:or (:and (start ?road ?city1)(end ?road ?city2))
(:and (start ?road ?city2)(end ?road ?city1)))))

17. Knowledge representation
K l d i
Ontology models domain knowledge
Semantic annotation
Ontology is used as a vocabulary, classification or
indexing schema for a collection of items
Semantic search
Ontology is used as a query vocabulary or for
gy q y y
query rewriting purposes
Configuration
Ontology defines correct configuration templates

18. Neutral Authoring Ontology‐based Search
Bases for application Ontologies provide the
development as core data structure for the navigation
model for all applications. of the results, support
browsing and classification.
Typical use case in AI.
Typical use case in AI
Ontologies allow for term
disambiguation and query
rewriting.
Ontologies
O t l i
Global view on information. Specification of software
systems and automation of
Organization and
code generation.
management of information
sources and their MDA.
interrelation.
SOA.
Consistency checking.
Common Access to Information Ontology‐based Specification
Source: Jasper & Uschold, 1999.

19. Query formulation.
f
Query rewriting.
Augmented query
d
processing.
Semantic matchmaking.
h k
Navigation and browsing.
Vizualization.
l

20. Watson
http://watson.kmi.open.ac.uk/Overview.html
Swoogle
http://swoogle.umbc.edu/
Protégé Ontologies
g g
http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies
OBO Foundation Ontologies
http://www.obofoundry.org/
Open Ontology Repository
http://ontolog.cim3.net/cgi‐bin/wiki.pl?OpenOntologyRepository
Manchester Ontology Library
http://owl.cs.manchester.ac.uk/repository/
Ontology Yellow Pages
http://wg.sti2.org/semtech‐onto/index.php/The_Ontology_Yellow_Pages
http://wg sti2 org/semtech onto/index php/The Ontology Yellow Pages
AIM@SHAPE
http://dsw.aimatshape.net/tutorials/ont‐intro.jsp
VoCamps
p
http://vocamp.org/wiki/Main_Page

22. Project management: controlling, planing, quality assurance etc.
t lli l i lit t
Domain analysis
Kno
motivating scenarios, competency questions, existing solutions
owledge acquisition
Documen
Conceptualization
Evaluat
conceptualization of the model, integration and extension of
existing solutions
ntation
tion
Implementation
implementation of the formal model in a representation language
Usage/Maintenance
adaptation of the ontology according to new requirements

23. Management Development oriented Support
Pre-development
Knowledge acquisition
Scheduling
g Environment study Feasibility study
Development
Evaluation Integration
Specification Conceptualization
Control Documentation Merging
Formalization Implementation
Post-development
Quality
assurance
Configuration Alignment
Maintenance Use
management

24. Historical Background
CommonKADS
Enterprise Ontology [Schreiber et al., 1999]
[Uschold & King, 1995]
[U h ld & Ki ]
DILIGENT
IDEF5 [Tempich, 2006]
[Tempich 2006]
[Benjamin et al. 1994]
OTK Holsapple&Joshi
H l l &J hi
[Holsapple & Joshi, 2002]
[Sure, 2003]
CO4 METHONTOLOGY
[Euzenat, 1995]
[Gomez‐Perez, 1996]

25. Two or more people interact and exchange
T l i d h
knowledge in order to build a common,
shared ontology in pursuit of a shared
of a shared,
collective, bounded goal.
Interaction may be indirect but required
but required.
Argumentation as a common interaction means.
Simple contributions not enough
not enough.
Bounded goal: beginning and end.
Collaborators may have individual goals.

26. 1.
1 Emergence of ideas. In the first phase, new concept ideas are collected in an
Emergence of ideas In the first phase new concept ideas are collected in an
ad‐hoc fashion. This is done using simple tags.
2. Consolidation in communities. The concept symbols generated in the first
p
phase are re‐used and adapted by the user community. The phase aims at
p y y p
extracting concepts from the available tags leading to a common terminology.
3. Formalization. This phase adds taxonomic and ad‐hoc relations to the
common terminology yielding lightweight but formal ontologies.
4. Axiomatization. The final step in the methodology addresses knowledge
workers, i.e. ontology engineers, to add axiom leading to a heavy‐weight
ontology.
[Braun, 2007]

27. Tagging is a very successful approach to
T i i f l h
organize all sorts of content on the Web.
Approaches to derive formal ontologies from
tag clouds are emerging.
There are several tools available for building
g g
ontologies using semantic wikis.
Serious games to engineer ontologies and
annotate content.
annotate content

28. http://vocamp.org/wiki/Main_Page

29. elena.simperl@sti2.at