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?
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