Eswcsummerschool2010 ontologies final

Vocabulary building (and alignment)

Elena Simperl

elena.simperl@kit.edu

www.kit.edu

A LITTLE HISTORY

KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)
2

ontology vocabulary
microformat conceptual graph

topic map thesaurus
schema
classification object model
semantic network
glossary taxonomy
3

Focus on knowledge
representation and
reasoning
Academic topic
Research prototypes
of ontology-based *
Standardization

4

Focus on data
integration,
community-driven
initiative on data
publishing

Community of
developers and
data and content
providers

Leveraging
maturing semantic
technologies, and
other trends (open
access)

5

It was never a simple matter

What exists?

What is?

What am I?

6 Ontologies and the Semantic Web / Ontologies - A Brief History - 6

And we’re back to where it all started

Greek etymology (ontos = of being; logia = science, study, theory)
Parmenides of Elea, ancient Greek philosopher, early 5th century
BC

“For never shall this prevail, that
things that are not are”

Parmenides made the ontological argument against nothingness,
essentially denying the possible existence of a void.
7 Ontologies and the Semantic Web / Ontologies - A Brief History - 7

Closer to our time

Jacob Lorhard, German philosopher (1561 - 1609)
First occurrence of the word Ontology (lat. Ontologia) and the first
published ontology in 1607

Translation from: Historical and conceptual foundations of diagrammatical ontology. P. Øhrstøm, S. Uckelman; H. Schärfe

8

Ontologies (or whatever you call them) in
Computer Science

An ontology defines Application areas
• Concepts
Natural language processing
• Relationships
• Any other distinctions that are relevant to Artificial intelligence
capture and model knowledge from a Digital libraries
domain of interest
Software engineering
Ontologies are used to Database design
Share a common understanding about a
domain among people or machines
Enable reuse of domain knowledge

This is achieved by
Agree on meaning and representation of
domain knowledge
Make domain assumptions explicit
Separate domain knowledge from the
operational knowledge

9

Agree on meaning and representation
(define-class Travel (?travel)
"A journey from place to place"
:axiom-def
( .... )
:iff-def
(and (arrivalDate ?travel Date)
(departureDate ?travel Date))
:def
(and (singleFare ?travel Number)
(companyName ?travel String)))

10

Make domain assumptions explicit

11

Separate domain and operational
knowledge

12

ONTOLOGIES AND SEMANTIC
TECHNOLOGIES
13

Semantic technologies revisited

Data is self-describing
Data items are inter-connected
Applications can derive new knowledge from existing
data

Advantages
Scalable interoperability
Enhanced information management
Flexible application engineering (if you have
proficient developers)
14

Semantic technologies at BestBuy

Goal: “to provide more
visibility to products,
services and locations to
humans and machines”
Search engines identify
the data more easily and
put it into context (30%
increase in search traffic)
Improved consumer
experience
Due to “Increasing product and service visibility through front-end semantic web” by Jan Myers, SemTech 2010
15

Semantic technologies at BestBuy

Data is marked-up
using RDFa and
refers to concepts
from a pre-defined
eCommerce ontology.
Markup is entered by
BestBuy staff via
online forms that
produce RDFa.

16

Semantic technologies in life sciences
Medical terminologies reflect a common
agreement on the types of things people
talk about in medical science, and their
properties and relationships.

Ontologies provide a specification of these
conceptual models using formal languages.

Advantages:
As a standardized vocabulary: facilitate
communication
Interoperability: standardization of data
exchange formats, automatized integration,
interlinking
Enhanced information management:
biological objects annotated using the
ontology; improved navigation and filtering.

17

Features of an ontology

Models knowledge about a specific domain

Reflects the shared understanding of a group of stakeholders
about that domain

Defines
A common vocabulary
The meaning of terms
How terms are interrelated

Consists of
Conceptualization and implementation

Contains
Ontological primitives: classes, instances, properties,
axioms/constraints…

18

Classifications of ontologies

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.
19

Classifications of ontologies (2)

Issue of the conceptualization
Upper-level/Top-level
Core
Domain
Task
Application
Representation
Degree of formality
Highly informal: in natural language
Semi-informal: in a restricted and structured form of natural
language
Semi-formal: in an artificial and formally defined language
Rigorously formal: in a language with formal semantics,
theorems and proofs of such properties as soundness and
completeness

20

Languages for building ontologies

Ontologies can be built using various languages with various
degrees of formality
Natural language
UML
ER
OWL/RDFS
WSML
FOL
...
The formalism and the language have an influence on the kind of
knowledge that can be represented, and inferred
A conceptual model is not necessarily a formal ontology only
because it is written in OWL

21

Are ontologies just UML?

Ontologies vs ER schemas
Semantic Web ontologies represented in Web-compatible languages,
use Web technologies
They represent a shared view over a domain
Ontologies vs UML diagrams
Formal semantics of ontology languages defined, languages with
feasible computational complexity available
Ontologies vs thesauri
Formal semantics, domain-specific relationships
Ontologies vs taxonomies
Richer property types, formal semantics of the is-a relationship

22

Did Linked Data kill ontologies?

23

Ontologies in the age of Linked Data
Publication according to Linked
Data principles
Trade-off between
acceptance/ease-of-use and
expressivity/usefulness
Human vs machine-oriented
consumption (using specific
technologies)
Stronger commitment to reuse
instead of development from scratch
Model pre-defined through the
(semi-) structure of the data to be
published
Emphasis on alignment, especially
at the instance level

24

ONTOLOGY ENGINEERING

HOW TO BUILD A
VOCABULARY
25

Methodologies

CommonKADS
[Schreiber et al., 1999]
Enterprise Ontology
[Uschold & King, 1995]
Holsapple&Joshi
IDEF5 [Holsapple & Joshi, 2002]
[Benjamin et al.
1994]
CO4
[Euzenat, 1995]
26

Methodologies related to Knowledge Management systems

The On-To-Knowledge methodology takes a pragmatic approach to ontology
Go / Sufficient Meets
No Go? requirements requirements Roll-out? Changes?
engineering and contains many useful tips to support non-experts to build
? ?

an ontology.
Common ORSD + Target Evaluated Evolved
KADS Semi-formal ontology ontology ontology
Worksheets ontology Human
description
Issues

Refine- Evalu-
Application Knowledge
Feasibility
Kickoff & Management
study ment ation
Evolution
Application

Software
Identify .. 5. Capture 7. Refine semi- 10. Technology- 13. Apply
1. Problems & requirements formal ontology focussed ontology Engineering
opportunities specification in description evaluation 14. Manage
2. Focus of KM ORSD 8. Formalize into 11. User- evolution and
application 6. Create semi- target ontology focussed maintenance
3. (OTK-) Tools formal ontology 9. Create evaluation
description Prototype 12. Ontology-
4. People
focussed
evaluation

Ontology Development

KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) Source: Sure, 2003.
27

Methodologies related to Software Engineering
Ontology Management
METHONTOLOGY contains the most comprehensive description of
Scheduling, controlling, quality assurance

ontology engineering activities. study targeted at ontology engineers.
Feasibility
It is
Problems, opportunities, potential solutions, economic feasibility

Knowledge acquisition
Domain analysis
motivating scenarios, competency questions, existing solutions

Ontology reuse
Ontology reuse
Documentation
Documentation

Evaluation
Evaluation

Conceptualization
conceptualization of the model, integration and extension of
existing solutions

Implementation
implementation of the formal model in a representation language

Maintenance
adaptation of the ontology according to new requirements

Use
ontology based search, integration, negotiation

Source: METHONTOLOGY, Gómez-Pérez, A. ,1996.
28

Collaborative methodologies

2. Local
Adaptation
1. Central O1 3. Central
Build Analysis
5. Local
Update

OI O-User 1
Ontology …
User Domain Ontology Board
Expert Engineer
On
O-User n

Knowledge
Engineer 4. Central
Revision

Source: DILIGENT: Tempich, 2006.
29

Newer approaches

Ontology engineering increasingly
becomes an community activity.
Employing Wikis in Tagging is a very
ontology engineering Usage of games with a successful approach to
enables easy purpose to motivate organize all sorts of
participation of the humans to undertake content on the Web.
community and lowers complex activities in the Tags often describe the
barriers of entry for ontology life cycle. meaning of the tagged
non-experts. Less suitable for content in one term.
So far less suitable for developing anything Approaches to derive
developing complex, that is not on a formal ontologies from
highly axiomatized mainstream topic tag clouds are
ontologies. emerging.

30

Condensed version
Documentation

Test (Evaluation)

Requirements analysis
motivating scenarios, use cases, existing solutions,
effort estimation, competency questions, application requirements

Glossary creation (Conceptualization)
conceptualization of the model, integration and extension of
existing solutions

Modeling (Implementation)
implementation of the formal model in a representation language

31

Issues to be considered

What is the ontology going to be used for?
Who will use the ontology?
How it will be maintained and by whom?
What kind of data items will refer to it? And how will these references be
created and maintained?
Are there any information sources available that could be reused?

To answer these questions, talk to domain experts, users, and software
designers.
Domain experts don‘t need to be technical, they need to know about the
domain, and help you understand its subtleties
Users teach you about the terminology that is actually used and the
information needs they have.
Software designers tell you tell you about the type of use cases you need
to handle, including the data to be described via the ontology

32

„Design for a world where Google is your
Example: BBC homepage, Wikipedia is your CMS, and
humans, software developers and
machines are your users“
Various micro-sites built and
maintained manually
No integration across sites in
terms of content and metadata
Use cases
Find and explore content on
specific (and related) topics
Maintain and re-organize sites
Leverage external resources

Ontology: One page per thing,
reusing DBpedia and
MusicBrainz IDs, different
labels…
http://www.slideshare.net/reduxd/beyond-the-polar-bear
33

Please stop building new ontologies…

REUSING EXISTING
KNOWLEDGE
34

Where to find ontologies
Swoogle: over 10 000 documents, across domains
http://swoogle.umbc.edu/

Protégé Ontologies: several hundreds of ontologies, across domains
http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies

Open Ontology Repository: work in progress, life sciences, but also other domains
http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository

Tones: 218 ontologies, life sciences and core ontologies.
http://owl.cs.manchester.ac.uk/repository/browser

Watson: several tens of thousands of documents, across domains
http://watson.kmi.open.ac.uk/Overview.html

Talis repository
http://schemacache.test.talis.com/Schemas/

Ontology Yellow Pages: around 100 ontologies, across domains
http://wg.sti2.org/semtech-onto/index.php/The_Ontology_Yellow_Pages

OBO Foundation Ontologies
http://www.obofoundry.org/

AIM@SHAPE
http://dsw.aimatshape.net/tutorials/ont-intro.jsp

VoCamps
http://vocamp.org/wiki/Main_Page
35

Swoogle functionality

36

Swoogle coverage

37

Protégé ontology library

38

Open ontology repository

Presentation:
http://ontolog.cim3.net/file/work/OOR/OOR_presentations_publications/OO
R-SemTech_Jun2010.pdf
Demo: http://oor-01.cim3.net/search

39

OBO Foundation ontologies

40

More resources

http://vocamp.org/wiki/Where_to_find_vocabularies

41

How to select the right ontology

What will the ontology be used for?
Does it need a natural language interface and if yes in which
language?
Do you have any knowledge representation constraints (language,
reasoning)?
What level of expressivity is required?
What level of granularity is required?
What will you reuse from it?
Vocabulary++
How will you reuse it?
Imports: transitive dependency between ontologies
Changes in imported ontologies can result in inconsistencies and
changes of meanings and interpretations, as well as computational
aspects.

42

How to select the right ontology (2)

The FOAF level: Use the simple ones, especially if they are used by others
as well
FOAF, DC, Freebase schemas…

The upper-level: Use upper-level ontologies, they are typically the result of
extensive discussions and considerations and allow you to ground your
more specific ontologies

Other knowledge structures: Use taxonomies, vocabularies and
folksonomies as a baseline, but encode using Semantic Web languages
(Make your results available to the community)

Ontology learning: Apply existing tools to create a baseline structure, then
revise and enrich
43

WordNet http://www.w3.org/TR/wordnet-rdf/

44

Freebase

45

Freebase (ii)

Schemas: concepts/types, properties and instances, similar to ontologies.

46

DBpedia

Extract structured information from Wikipedia and to make this information
available on the Web
2.9 million things, 282,000 persons, 339,000 places (including 241,000
populated places), 88,000 music albums, 44,000 films, 15,000 video
games, 119,000 organizations (including 20,000 companies and
29,000 educational institutions), 130,000 species, 4400 diseases
Ontology backbone
259 classes arranged in a subsumption hierarchy with altogether 1200
properties
Overview of all classes at
http://mappings.dbpedia.org/server/ontology/classes
Infobox-to-ontology and the table-to-ontology mappings

47

GoodRelations

48

Other approaches

Create RDF data from existing resources
http://simile.mit.edu/wiki/RDFizers
http://esw.w3.org/ConverterToRdf
Schema mappings have to be configured manually.
Some issues to be considered
Open vs closed world assumption
Semantics of the is-a relationship
Expressivity: n-ary relatioships, attributes of relatotionships…

Enrich folksonomies: ambiguities, spelling variants and errors,
abbreviations, multilinguality…

49

Ontology engineering today

Various domains and application scenarios: life sciences, eCommerce,
Linked Open Data
Engineering by reuse for most domains based on existing data and
vocabularies
Alignment of data sets
Data curation
Human-aided computation (e.g., games, crowdsourcing)
Most of them much simpler and easier to understand than the often cited
examples from the 90s
However, still difficult to use (e.g., for mark-up)

50

Ontology engineering today (2)

Back to the BBC example

51

Ontology engineering today (3)
Management Development oriented Support

Pre-development

Scheduling Environment study Feasibility study

Development
Evaluation Integration

Specification Conceptualization
Control Documentation Merging

Formalization Implementation

Post-development

Quality
assurance
Configuration Alignment
Maintenance Use
management

52

Open topics

Meanwhile we have a better understanding of the scenarios which benefit
from the usage of semantics and the technologies they typically deploy.
Guidelines and how-to‘s
Design principles and patterns
Schema-level alignment (data-driven)
Vocabulary evolution
Assessment and evaluation
Large-scale approaches to knowledge elicitation based on combinations of
human and computational intelligence.

53

Modeling hands-on

www.kit.edu

Design principles

Abstraction
Ignoring certain aspects in order to simplify the handling of something
or to better understand other aspects
The modeler decides what it is important or not and then chooses a
representation that is more tractable than the original
A representation of something cannot be greater than that something

Models should be divisible
Model modules should be highly cohesive and have low coupling
Use informative labels

55
55

The very basics
constraint

relationship
Some important thing Other important thing

The node is a non-trivial thing, easy to find in the domain, with a
technological equivalent, with high cohesion and low coupling
Candidates for nodes:
 things or entities in ER models, knowledge bases
 classes in OO models
 types
states in state machine diagrams etc
Relationships/associations/relations/properties/attributes hold between
instances of the entities.
Constraints/axioms/restrictions/rules further specify the nature of
relationships.

56

Classes

A class represents a set of instances

A class should be highly cohesive, precisely nameable, relevant

A class should have a strong identity

Crime Suspect

57

How to find classes

Typical candidates: NOUNS
Actors of use cases do not necessarily correspond to classes

Other terms as well
Verbs
An association which starts to take on attributes and associations of its own
turns into an entity: „Officer arrests suspect“
Events: „Being ill“  „Illness episode“
Passive form: re-formulate in active form
No pronouns

58
58

Cohesion and identity

A class should represent one thing, all of that thing and nothing but that
thing
You can prove cohesion by
Giving the class a representative name
Noun (+ adjective, sometimes however also captured as attribute
value)
Blackmail victim, robbery victim
Blue car, red car
Cars is not cohesive

Avoid ambiguous terms
Manager, handler, processor, list, information, item, data…
Identity ~ individuality: classes change values, but are still the same entity
Child/Adult: age

59
59

Relevance

Goint out too far vs. going down too far

Investigate homonyms and synonims
Can medicine and drug be considered synonims?
Do they have the same
properties/characteristics/attributes/relationships?
Do they have a critical mass of commonalities?

60
60

Characterizing classes

Two types of principal characteristics
Measurable properties: attributes
Inter-entity connections: relationships, associations

Arrest details as attribute of the suspect vs. Arrest as a class vs Arrest
as a relationship
Do we measure degrees of arrestedness or do we want to be able to
distinguish between arrests?

Color of an image as attribute vs. class

A „pointing finger“ rather than a „ruler“ indicates identity

61
61

Attributes

An attribute is a measurable property of a class
Scalar values: choice from a range of possibilities
An attribute is NOT a data structure. It is not complicated to measure

Value of attributes: integer, real numbers, enumerations, text…

Witness
Attributes do NOT exhibit identity
name:text
age: integer
eyesight:
Attributes should have precise representative names enum{…}

62
62

How to find attributes

Nouns in „-ness“
Velocity-ness, job-ness, arrested-ness…

„How much, how many“ test.
If you evaluate this, then it is probably an attribute
If you enumerate these, it is probably a class

Range of attributes
Age abstracted as an integer
Latitude and longitude: real numbers/NSEW
Names abstracted as text

63

Relationships

Crime Suspect

1
copycat
Some instances
Crime of a class hold a
*
relationship with
some instances
0..1 0..* of another class.
Person Vehicle

* *
Crime Officer
investigates
64
64

How to find relationships

Verbs, verbal phrases and things that could have been verbs.
„The butler murdered the duchess“

Properties
reflexivity, cardinality, functional, inverse-functional, discountinuous
multiplicity, many-to-many, all values from, some values of, transitivity,
symmetry etc.

Roles
Nouns, adjectives.
Verbs: indication of time‘s passing.
Short-term, one-to-one associations should be named with present participles.
Longer-term, one-to-many associations should be named with past participles,
or with the simple present third-person singular.

65
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Examples

* *
Crime Officer
investigated

* *
Crime Officer
investigating

is investigated

Crime * * Officer
investigating

66

Is-a hierarchy

Top-down, bottom-up, middle-out

Are all instances of entity A also instances of entity B?

Are all A‘s also B‘s?

Roles

Difference between classifications, associations, and aggregations

67

Examples

Bill MealOrder

Dish Menu

Bed Mattress

Diary Appointment

Crime CrimeScene

68

Overloading
subsumption
Instantiation
Thing vs model

Composition
Is-a vs part-of

Constitution
Thing vs what matter is it made of

Examples due to Chris Welty, IBM Research
69

Assignment: Modeling

“San Francisco Opera is the second largest opera company in North
America. Gaetano Merola and Kurt Herbert Adler were the Company’s first two
general directors. Merola led the Company from its founding in 1923 until his
death in 1953; Adler was in charge from 1953 through 1981. Legendary for
both their conducting and managerial skills, the two leaders established a
formidable institution that is internationally recognized as one of the top opera
companies in the world—heralded for its first-rate productions and roster of
international opera stars. Following Adler’s tenure, the Company was headed
by three visionary leaders: Terence A. McEwen (1982–1988), Lotfi Mansouri
(1988–2001), and Pamela Rosenberg (2001–2005). Originally presented over
two weeks, the Company’s season now contains approximately seventy-five
performances of ten operas between September and July. San Francisco
Opera celebrated the 75th anniversary of its performing home, the War
Memorial Opera House, in 2007 . The venerable beaux arts building was
inaugurated on October 15, 1932 and holds the distinction of being the first
American opera house that was not built by and for a small group of wealthy
patrons; the funding came thanks to a group of private citizens who
encouraged thousands of San Franciscans to subscribe. The War Memorial
currently welcomes some 500,000 patrons annually.”

70

Assignment: Encoding in OWL

From
http://www.jfsowa.com/ontology/

71

Assignment: Alignment

The aim is to reach a ‚shared conceptualization‘ of all participants at the
ESWC2011 summer school on the ontology developed in the previous
assigment.
Assumption: every group is committed to their conceptualization.
Procedure: each group selects a representative, representatives
agree on an editor, and on the actual steps to be followed.

72

Eswcsummerschool2010 ontologies final

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