The Basics of Ontologies

The Basics of Ontologies


Nordic Agricultural Ontology Service (AOS) Workshop


Royal Veterinary and Agricultural University


Copenhagen, Denmark



February 28, 2003





Frehiwot Fisseha


Frehiwot.Fisseha@fao.org


What this talk is all about

1.
The Origin of Ontology


2.
The Definitions of Ontology


3.
Motivation for Developing Ontology


4.
Some Examples


5.
Benefits of Ontology


6.
Application Areas of Ontology


7.
Types of Ontology


8.
Similarities and Differences of Ontologies and Thesauri


9.
Things to keep in mind


10.
Conclusion

The

term

“
ontology
”

has

been

used

for

a

number

of

years

by

the

artificial

intelligence

and

knowledge

representation

community

but

is

now

becoming

part

of

the

standard

terminology

of

a

much

wider

community

including

information

systems

modelling
.


The

term

is

borrowed

from

philosophy
,

where

ontology

mean

‘a

systematic

account

of

existence’
.

(Not

very

useful

definition

for

our

purpose!!)


The Origin of Ontology

What is Ontology? (1)

An ontology is "the specification of conceptualizations, used to help programs and
humans share knowledge."


An ontology is a set of concepts
-

such as things, events, and relations that are
specified in some way in order to create an agreed
-
upon vocabulary for exchanging
information.
(Tom Gruber, an AI specialist at Stanford University.)


Ontologies establish a joint terminology between members of a community of interest.
These members can be human or automated agents.


•
In information management and knowledge sharing arena, ontology
can be defined as follows:


–
An ontology is a
vocabulary of concepts

and
relations

rich enough to enable
us to express knowledge and intention without
semantic ambiguity.


–
Ontology describes domain knowledge and provides an agreed
-
upon
understanding of a domain.


–
Ontologies
: are collections of statements written in a language such as RDF
that
define the relations between concepts

and
specify logical rules for
reasoning

about them.

•
Computers will "understand" the meaning of semantic data on a web page by
following links to specified ontologies.

What is

Ontology? (2)


What is Ontology?(3)

A more formal definition is:


“An ontology is a formal, explicit specification of a shared conceptualization”
(Tom Gruber)


–
“
explicit
” means that “the type of concepts used and the constraints on their use are
explicitly defined”;

–
“
formal
” refers to the fact that “it should be machine readable”;

–
“
shared
” refers to the fact that the knowledge represented in an ontology are agreed
upon and accepted by a group”;

–
“
conceptualization
” refers to an abstract model that consists the relevant concepts and
the relationships that exists in a certain situation


The basis of ontology is CONCEPTUALIZATION. Consider the following:


The conceptualization consists of

-
the identified concepts (objects, events, beliefs, etc)

-
E.g. Concepts
: disease, symptoms, therapy

-
the conceptual relationships that are assumed to exist and to be relevant.

-
E.g. Relationships
: “disease causes symptoms”, “therapy treats disease”


World without ontology = Ambiguity


Example (1)

Ambiguity for computer

Rice?

–
International
Rice

Research Institute

–
Rice

Research Program

–
Rice

Carrier Service Center

–
Africa
Rice

Center

–
Rice

University


Cook?

You mean

–
chef

–
information about how to cook something,

–
or simply a place, person, business or some other entity with "cook" in its name.


The problem is that the word “
rice
“ or “
cook
” has no meaning, or semantic content, to the
computer.

World without ontology = Ambiguity


Example (2)

Ambiguity for humans





Cat


The Vet and Grand ma associate different view for the concept cat.

Motivation (1)

The

reason

for

ontologies

becoming

so

important

is

that

currently

we

lack

standards

(shared

knowledge)

which

are

rich

in

semantics

and

represented

in

machine

understandable

form
.

Ying Ding, Ontoweb


Ontologies

have

been

proposed

to

solve

the

problems

that

arise

from

using

different

terminology

to

refer

to

the

same

concept

or

using

the

same

term

to

refer

to

different

concepts
.



Howard

Beck

and

Helena

Sofia

Pinto




Motivation (2)

•
Inability to use the abundant information resources on the web

The

WEB

has

tremendous

collection

of

useful

information

however

getting

information

from

the

web

is

difficult
.

Search engines are restricted to simple keyword based techniques. Interpretation of information
contained in web documents is left to the human user.


•
Difficulty in Information Integration

The integration of data from various sources is a challenging task because of synonyms and
homonyms.


•
Problem in Knowledge Management

Multi
-
actor scenario involved in distributed information production and management.

“People as well as machines can‘t share knowledge if they do not speak a common language

[T. Davenport]


Ontologies

provide the required
conceptualizations
and

knowledge representation

to
meet these challenges.



Motivation (3)

•
Database
-
style queries are effective


–
Find red cars, 1993 or newer, < $5,000

•
Select

*
From

Car
Where

Color=“red”
And

Year >= 1993
And

Price < 5000


•
Web is not a database

–
Uses keyword search

–
Retrieves documents, not records



Ontologies

provide the required
knowledge

and
representation

to search the web in a database fashion
through implicit Boolean search.




What do ontologies look like?

Example: Car
-
Ad Ontology

Year

Price

Make

Mileage

Model

Feature

PhoneNr

Extension

Car

has

has

has

has

is for

has

has

has

1..*

0..1

1..*

1..*

1..*

1..*

1..*

1..*

0..1

0..1

0..1

0..1

0..1

0..1

0..*

1..*

Graphical

Car [0:1] has Year [1:*];

Year {regexp[2]: “
\
d{2} :
\
b’
\
d{2}
\
b, … };

Car [0:1] has Make [1:*];

Make {regexp[10]: “
\
bchev
\
b”, “
\
bchevy
\
b”, … };

Car [0:1] has Model [1:*];

Model {…};

Car [0:1] has Mileage [1:*];

Mileage {regexp[8] “
\
b[1
-
9]
\
d{1,2}k”,


“1
-
9]
\
d?,
\
d{3} : [^
\
$
\
d][1
-
9]
\
d?,
\
d{3}[^
\
d]” }


{context: “
\
bmiles
\
b”, “
\
bmi
\
.”, “
\
bmi
\
b”};

Car [0:*] has Feature [1:*];

Feature {regexp[20]:


--

Colors


“
\
baqua
\
s+metallic
\
b”, “
\
bbeige
\
b”, …


--

Transmission


“(5|6)
\
s*spd
\
b”, “auto :
\
bauto(
\
.|,)”,


--

Accessories


“
\
broof
\
s+rack
\
b”, “
\
bspoiler
\
b”, …

...

Textual

Example: People Ontology

http://www.sciam.com/article.cfm?articleid=0005DE0B
-
2C93
-
1CBF
-
B4A8809EC588EEDF



Benefits of Ontology





•
To facilitate communications among people and organisations


aid to human communication and shared understanding by specifying meaning


•
To facilitate communications among systems with out semantic ambiguity. i,e to
achieve inter
-
operability


•
To provide foundations to build other ontologies (reuse)


•
To save time and effort in building similar knowledge systems (sharing)


•
To make domain assumptions explicit


Ontological analysis


clarifies the structure of knowledge


allow domain knowledge to be explicitly defined and described



•
Information Retrieval


As a tool for intelligent search through inference mechanism instead of keyword matching


Easy retrievability of information without using complicated Boolean logic


Cross Language Information Retrieval


Improve recall by query expansion through the synonymy relations


Improve precision through Word Sense Disambiguation (identification of the relevant meaning of a
word in a given context among all its possible meanings)

•
Digital Libraries


Building dynamical catalogues from machine readable meta data


Automatic indexing and
annotation of web pages or documents with meaning


To give context based organisation (semantic clustering) of information resources


Site organization and navigational support

•
Information Integration



Seamless integration of information from different websites and databases

•
Knowledge Engineering and Management


As a knowledge management tools for selective semantic access (meaning oriented access)


Guided discovery of knowledge

•
Natural Language Processing


Better machine translation


Queries using natural language


Application Areas of Ontologies

Types of Ontologies

Ontologeis can be classfied according to the degree of conceptualization


•
Top
-
level ontologies

•
describes very
general notions

which are
independent of a particular problem or domain


•
are applicable across domains and includes vocabulary related to things, events, time, space, etc



•
Domain ontologies

•
knowledge represented in this kind of ontologies is
specific to a particular domain

such as forestry,
fishery, etc.

•
They provide vocabularies about concepts in a domain and their relationships or about the theories
governing the domain.



•
Application or task ontologies

•
describe knowledge pieces depending both on a particular domain and task.

•
Therefore, they are related to
problem solving methods
.


Complexity of Ontologies

Depending on the wide range of tasks to which the ontologies are put ontologies can vary in
their complexity


Ontologies range from simple taxonomies to highly tangled networks including constraints
associated with concepts and relations.



•
Light
-
weight Ontology

•
concepts

•
‘is
-
a’ hierarchy among concepts

•
relations between concepts


•
Heavy
-
weight Ontology

•
cardinality constraints

•
taxonomy of relations

•
Axioms (restrictions)



Thesauri and Ontology

Similarities


•
Both serve the same purpose, namely to provide a shared conceptualisation about a
specific part of the world to different users in order to facilitate an efficient communication of
complex knowledge.


•
Both disciplines are based on concept systems representing highly complex knowledge
independent of any language.


•
Both are concerned about covering a broad range of terminology used in a particular
domain, and in understanding the relationships among these terms.


•
Both utilize a hierarchical organization to group terms into categories and subcategories.


•
Both can be applied to cataloguing and organizing information.

Thesauri and Ontology

Differences

•
Formality of the definition:


–
Thesauri uses text in natural language to define the meaning of terms. The correct
interpretation of the intended meaning depends on the user.

–
Ontologies specify conceptual knowledge explicitly using a formal language with clear
semantics, which allows an unambiguous interpretation of terms.


•
Computational support:

–
The available tools are quite different for thesauri and ontologies.

–
Most thesauri maintenance tools provide limited or no means for an explicit
representation of knowledge.

–
Ontology maintenance tools provide systems with powerful knowledge representation
languages and inference mechanisms that allow formal consistency checks, inference
of new knowledge, and a more user
-
friendly interaction.

•
Users:

–
Thesauri are intended for human users, where domain experts constitute the major user
group.

–
Ontologies are mainly developed for knowledge sharing between (both human and
artificial) agents.




•
Little possibility of re
-
use due to inherent semantic ambiguity and lack of the explicitness of
their semantics .


•
Difficulties in the diversity of their representational form (no common representational
language)


•
Developed for human use. They lack of expressive mechanisms to represent, maintain, and
reason about complex knowledge in an explicit form
-

interpretation is left for humans.




(Source:http://www.xmluk.org/slides/magic
-
circle_2002/wilson/XML_UK_SW_Thes/all.htm)




Reasons to evolve thesauri to ontologies


Problems with Thesaurus Modelling

BT/NT relations
-
AGROVOC


Thesauri have not been constructed with
purely defined semantics. It is common
for BT/NT relations within a thesauri to
include at least:


•
subtype of

(e.g. soil/ subsoil)


•
instance

(e.g. Development Agency/IDRC))


•
part of

(e.g. soil/top soil)


•
role

(e.g. Development Agency/Voluntary
agency)


•
property of

(e.g. maize/sweet corn)



•
MAIZE

•



NT
dent maize




NT
flint maize




NT
popcorn




NT
soft maize




NT
sweet corn




NT
waxy maize



•
SOIL

•
NT
top soil


NT
subsoil


•
Development Agencies

•
NT
development banks

NT
voluntary agencies

NT
IDRC



Problems with Thesaurus Modelling


Equivalence relations
–

UF, USE

•
UF/USE
-

between the descriptor and the non
-
descriptor (s).


•
Associative relationship can represent:

–
genuine synonymy, or
identical

meanings;

–
near
-

synonymy, or
similar

meanings;

–
In some thesaurus, antonym, or
opposite

meanings; ( eg. Eurovoc)



•
DEVELOPMENT

AGENCIES


•

UF
aid institutions
1


1
-

Similar but not necessarily identical
concept

Problems with Thesaurus Modelling

Associative relations
-

RT


The RT associative relation is more even open to interpretation than the hierarchical relation

For some thesaurus, it can contain:

–
cause and effect

–
agency or instrument

–
hierarchy
-

where polyhierarchy has not been allowed the missing hierarchical relationships are
replaced by associative relationships

–
sequence in time or space

–
constituent elements

–
characteristic feature

–
object of an action, process or discipline

–
location

–
similarity (in cases where two near
-
synonyms have been included as descriptors)

–
antonym


RT in AGROVOC

•
Degradation


•
RT
chemical reactions
1

RT
discoloration

RT
hydrolysis


RT
shrinkage


•
MAIZE

•

RT
corn flour


RT
corn starch
2


RT
zea mays


•
IDRC

•
BT
development agencies

RT
canada
3




1
-

cause and effect

2
-

characteristic feature

3
-

location


Thesauri and Ontology

how to migrate

•
Analyze the existing relations and establish semantically meaningful
relations:

–
BT/NT => ‘Is
-
A’ relation

–
RT => analyzed to roles/properties/attributes



(like “produces”, “used by”, “made for”).

•
Allow for machine
-
processable definitions:

–

Fencing sword = sword used for: fencing”

–

Weapon = object used for: fighting or hunting

–

Mother = human & female & which has born: human


•
There is no one correct way to model a domain

–
Modeling the required knowledge heavily relies on to what purpose the ontology will be
used.

•
Ontology development is a collaborative process

–
Knowledge captured in the ontology should be derived from consensus. This will ensure
reuse and share
-
ability.

•
Ontology works in a network fashion

–
No single ontology but networks of ontologies

•
Ontology development is necessarily a dynamic and iterative
process

–
Ontologies should evolve through time

Things to keep in mind....

Conclusion



•
Ontology provides better semantic representation and machine understandable
representation of knowledge.


•
Ontologies are natural successors of thesauri particular for information retrieval and
knowledge management.


•
Developing thesauri to ontologies requires increased precision of the semantics of the
existing relations in thesauri.


•
Ontology repositories will be distributed on the Web

–
methods and tools for accessing/reusing/aligning ontology's are needed.



Thank you for your attention !



Any questions, comments?