Logics for Data and

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Logics for Data and

Knowledge Representation

The DERA methodology for the development of
domain ontologies

Feroz Farazi

Originally by Fausto Giunchiglia and Biswanath Dutta

Modified by Feroz Farazi

Knowledge Representation (KR)


Abstraction of the world via models, of a particular
domain or problem, which allow automatic reasoning
and interpretation



Fundamental Goal


to represent knowledge in a manner that facilitates
inferencing new knowledge (i.e. drawing conclusions)
from the already known facts possibly encoded in a
knowledge base

2


According to (Crawford & Kuipers, 1990): A
knowledge representation system must have


a reasonably compact syntax


a well defined semantics so that one can say
precisely what is being represented


sufficient expressive power to represent human
knowledge


an efficient, powerful and understandable reasoning
mechanism


support in building large knowledge bases





3

Knowledge Representation Properties

Knowledge Representation Issues


KR issues:


How do people represent knowledge?


What is the nature of knowledge?


Do we have domain specific schema or generic, domain
independent schema?


How much it needs to be expressive?



4

Ontology


“formal, explicit specification of a shared
conceptualisation” [
T. R. Gruber, 1993
]



Models a domain consisting of a shared vocabulary
with the definition of objects and/or concepts and their
properties and relations



A structural framework for organizing information, and


used as a form of KR in the fields like, AI, SW, Lib. Sc., Inf.
Architecture, etc.



Can be used also as a language resource

5

Ontology Properties


Some of the ontological properties are:



Extendable



Reusable



Flexible



Robust




6

Domain


An area of knowledge or field of study that we are
interested in or that we are communicating about



Example:


Computer science, Artificial Intelligence, Soft computing,
Social networks, …Library science, Mathematics,
Physics, Chemistry, Agriculture, Geography, …


Music, Movie, Sculpture, Painting, …Food, Wine,
Cheese, …Space,…

7

Domain


A domain can be decomposed into its several
constituents, and


Each of them denotes a different
aspect

of entities



An example from Space domain
:
by region, by body
of water, by landform, by populated places, by
administrative division, by land, by agricultural land,
by facility, by altitude, by climate,




Each of these
aspects

is called
facet

8

Facet


A hierarchy of homogeneous terms describing an
aspect of the domain, where each term in the
hierarchy denotes a different concept



E.g.
,


Body of water(e.g., River, Lake, Pond, Canal), Landform
(e.g., mountain, hill, ridge), facility (e.g., house, hut,
farmhouse, hotel, resort), etc.


language facet (e.g., English, Hindi, Italian,), property
facet, author facet, religion facet (e.g., Christian, Hindu,
Muslim), commodity facet, etc.

DERA


a facet based knowledge organization framework


independent from any specific domain


allows building domain specific ontologies


mapping to Description Logic


logically sound


decidable



Developed by the UniTn KnowDive group

10

DERA Surface Structure


In the surface level, it has the following components:


D


Domain


E


Entity


R


Relation


A


Attribute



11

Domain (D)


A DERA domain is a tuple of,










D = <E, R, A>





Entity (E)


an elementary component that consists of
entity
classes

and their
instances
, having either perceptual
correlates or only conceptual existence in a domain
in context. It can be represented as a pair





E = <
C
,
E'
>


Where,


C

= a set of entity classes or concepts representing the
entities


E'

= a set of entities (also called
objects, instances or
individuals
), possibly, real world
named

entities, those
are the instantiations of
C



12

Entity (E)


Entity classes (C)
:


Represent the essence of the domain under
consideration;


Consist of the core classes representing a domain in
context


E.g.,

Consider the following classes in context of Space
domain:


Mountain, Hill, Lake, River, Canal, Province, City, Hotel,...

13

Entity (E)


Entity (E')
:


the real world named entities


representations of the real world entities



E.g.,



The Himalaya, Monte Bondone, Lake Garda, Trento, Povo, Hotel
America,...



14

Entity (E)

15

An example from the Space domain

Relation (R)


An elementary component consists of classes
representing relations between entities






R = <{r}>



{r} is a set of relations


A
relation

r is a link between two entities (E')


Builds a semantic relation between the entities



E.g.
,


Some relations (spatial) from Space domain: near,
adjacent, inside, before, center, sideways, etc.

16

Attribute (A)


An elementary component consists of classes
expressing the characteristics of entities








A = <
A'
,
C
>


Where
A'

is a set of datatype attributes and
C

is a set
of descriptive attributes


An attribute is any property, qualitative, quantitative
or descriptive measure of an entity



17

Attribute (A) (contd…)


Datatype Attributes (A')
:


The datatype attributes include the attribute classes that
account the
quality

or
quantity

of an entity within a
domain



E.g.
,


latitude, longitude
(of a place):


45
0

N, 18
0

S


altitude
(of a mountain):


8000ft, 2400m.


high, low


depth

(of a lake):


deep, shallow


100ft., 20m.

18

Attribute (A) (contd…)


Descriptive Attributes (
C
)
:


include the attribute classes that describe the entities under a
domain in consideration


value could consist of a single string (single valued) or a set
of strings (multivalued)



E.g.
,


natural resource

(of a place):


coal, natural gas, oil, …


architectural style

(of a castle):


{Classical architecture, Greek architecture, Roman architecture, Bauhaus,
etc.}


history

(of a place)


……….


climbing route

(to a mountain)


……………….

19

Mapping


From
DERA

to
DL



Entity classes (C)
-
> Concepts


Relations (R)
-
> Roles


Datatype attributes (A')
-
> Roles


Descriptive attributes (
C
)
-
> Roles


Entity (E')
-
> Individuals



20

Methodology


Step

1
:

Identification

of

the

atomic

concepts


Step

2
:

Analysis

(per

genus

et

differentiam)


Step

3
:

Synthesis


Step

4
:

Standardization


Step

5
:

Ordering



Following

the

above

steps

leads

to

the

creation

of

a

set

of

facets
.

They

constitute

a

faceted

representation

scheme

for

a

domain

21

Ontological Principle


Relevance

(e
.
g
.
,
breed

is

more

realistic

to

classify

the

universe

of

cows

instead

of

by

grade
)


Ascertainability

(e
.
g
.
,

flowing

body

of

water)


Permanence

(e
.
g
.
,

Spring
-

a

natural

flow

of

ground

water)


Exhaustiveness

(e
.
g
.
,

to

classify

the

universe

of

people
,

we

need

both

male

and

female
)


Exclusiveness

(e
.
g
.
,

age

and

date

of

birth
,

both

produce

the

same

divisions)


Context

(e
.
g
.
,

bank
,

a

bank

of

a

river,

OR,

a

building

of

a

financial

institution)


Important
:

helps

in

reducing

the

homographs


Currency

(e
.
g
.
,

metro

station

vs
.

subway

station)


Reticence

(e
.
g
.
,

minority

author)


Ordering


Important
:

ordering

carries

semantics

as

it

provides

implicit

relations

between

the

coordinate

terms


22

Identification of the atomic concepts


Sources of the concepts


WordNet


GeoNames


TGN


Literature



23

Identification of the atomic concepts


Some

of

the

relevant

sub
-
trees

in

WordNet

are
:


location


artifact,

artefact


body

of

water,

water


geological

formation,

formation


land,

ground,

soil


land,

dry

land,

earth,

ground,

solid

ground,

terra

firma


Note
:

not

necessarily

all

the

nodes

in

these

sub
-
trees

need

to

be

part

of

the

space

domain
.

For

example,

the

descendants

of

artifact
,

like,

article,

anachronism,

block
,

etc
.

are

not
.

24

Hill

Stream

River


the

well

defined

elevated

land


formed

by

the

geological

formation

(where

geological

formation

is

a

natural

phenomenon)


altitude

in

general

>
500
m


the

well

defined

elevated

land


formed

by

the

geological

formation
,

where

geological

formation

is

a

natural

phenomenon


altitude

in

general

<
500
m


a

body

of

water


a

flowing

body

of

water


no

fixed

boundary


confined

within

a

bed

and

stream

banks


a

body

of

water


a

flowing

body

of

water


no

fixed

boundary


confined

within

a

bed

and

stream

banks


larger

than

a

brook

Mountain

Analysis

25

Body

of

water



Flowing

body

of

water


Stream



Brook



River


Stagnant

body

of

water


Pond


Landform



Natural

depression


Oceanic

depression



Oceanic

valley



Oceanic

trough


Continental

depression



Trough



Valley


Natural

elevation


Oceanic

elevation



Seamount



Submarine

hill


Continental

elevation



Hill



Mountain

* each term in the above has gloss and is linked to synonym(ous) terms in the knowledge base

Synthesis

26


Space [
Domain
]


by geographical feature [
Entity class
]


by water formation


by land formation


by land


by administrative division





by relations [
Relation
]


spatial relation


direction, internal, external, longitudinal, sideways, etc.


functional relation (e.g., primary inflow, primary outflow)





by attribute


[
Datatype attribute
]


latitude


Longitude


dimension





[
Descriptive attribute
]


Natural resource


Architectural style


Time zone


ph


History




Facets and sub
-
facets

27

Log
-
in
: http://uk.disi.unitn.it/resources/html/UKDomain.html

References


F. Giunchiglia and B. Dutta.
DERA: A Faceted Knowledge Organization
Framework
. Technical report, KnowDive, DISI, University of Trento,
2010.


B. Dutta, F. Giunchiglia, V. Maltese,
A facet
-
based methodology for geo
-
spatial modelling
, GEOS, 2011.


Crawford, J. M. & Kuipers, B. (1990).
ALL: Formalizing Access Limited
Reasoning. Principles of semantic networks: Explorations in the
representation of knowledge
, Morgan Kaufmann Pub., 299
-
330.


S. R. Ranganathan.
Prolegomena to Library Classification
. Asia
Publishing House, 1967.


T. R. Gruber.
A translation approach to portable ontologies
. Knowledge
Acquisition, 5(2):199
-
220, 1993.