from knowledge organization to knowledge representation (isko uk conference)
DESCRIPTION
So far, within the Library and Information Science (LIS) community, Knowledge Organization (KO) has developed its own very successful solutions to document search, allowing for the classification and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the Artificial Intelligence and Semantic Web communities, Knowledge Representation (KR), has developed very powerful end expressive techniques which, via the use of ontologies, support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology, inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.TRANSCRIPT
From Knowledge Organization
(KO) to Knowledge
Representation (KR)
Fausto Giunchiglia, Biswanath Dutta, Vincenzo
Maltese
ISKO UK11/26/2013
2
Know
ledge O
rganiz
ati
on:
make
info
rmati
on
ava
ilable
LIS developed its own very successful solutions for the classification and search of documents.
In KO, search is focused on document properties (e.g. title, author, subject).
KO tends to fail in situations when users express their needs in terms of entity properties (e.g., of the author)
ISKO UK11/26/2013
3Li
mit
ati
ons
in K
O:
Lack
of
expre
ssiv
eness
Search by document propertiesGive me documents with subject “Michelangelo” and “marble sculpture”
Search by entity propertiesGive me documents about marble sculptures made by a person born in Italy
ISKO UK11/26/2013
4Li
mit
ati
ons
in K
O:
Lack
of
form
aliz
ati
on
Subject: Buonarroti, Michelangelo
Subject: sculpture – Renaissance
In indexes the syntax of the subjects is given by a subject indexing language grammar.
What about the semantics?
• Is Michelangelo the Italian artist? When and where he was born? What are his most famous works?
• Is sculpture the form of art?
• Is Renaissance the historical period? When and where exactly?
ISKO UK11/26/2013
5Fr
om
docu
ments
to
enti
ties
Name: David
Class: statueAuthor: MichelangeloDate of creation: 1504Matter: marbleHeight: 4.10 m
Name: Michelangelo Surname: Buonarroti
Class: artistDate of birth: 6th March 1475Date of death: 18th February 1564
ISKO UK11/26/2013
6
From
KO
to K
RKR
search by any entity property
KOsearch by
title, author, subject
ISKO UK11/26/2013
7
Cla
ssifi
cati
on
Onto
logie
s
ISKO UK11/26/2013
8D
esc
ripti
ve O
nto
logie
s:
inte
nsi
onal k
now
ledge
ISKO UK11/26/2013
9D
esc
ripti
ve O
nto
logie
s:
exte
nsi
onal k
now
ledge
ISKO UK11/26/2013
10D
esc
ripti
ve O
nto
logie
s:
exte
nsi
onal k
now
ledge
Give me documents about any lake with depth greater than 100 written by Italians
ISKO UK11/26/2013
11A
new
appro
ach
: D
ER
A How to build high quality and scalable descriptive ontologies?
DERA is faceted as it is inspired to the principles and canons of the faceted approach by Ranganathan
DERA is a KR approach as it models entities of a domain (D) by their entity classes (E), relations (R) and attributes (A)
ISKO UK11/26/2013
12D
esc
ripti
ve O
nto
logie
s in
DERA
(I)
Step 1: Identification of the atomic concepts (E) watercourse, stream: a natural body of running water flowing on or under the earth
Step 2: Analysis a body of watera flowing body of waterno fixed boundaryconfined within a bed and stream bankslarger than a brook
ISKO UK11/26/2013
13D
esc
ripti
ve O
nto
logie
s in
DERA
(II)
Step 3: Synthesis.
(E) Body of water (is-a) Flowing body of water (is-a) Stream (is-a) Brook (is-a) River (is-a) Still body of water (is-a) Pond (is-a) Lake Step 4: Standardization. (E) stream, watercourse: a natural body of running water flowing on or under the earth
ISKO UK11/26/2013
14D
esc
ripti
ve O
nto
logie
s in
DERA
(III
)Step 5: Ordering
Terms and concepts in the facets are ordered
Step 6: Formalization
Descriptive ontologies are translated into Description Logic formal ontologies, e.g.,:
River ⊑ StreamRiver (Volga)Length (Volga, 3692)
ISKO UK11/26/2013
15
Propert
ies
of
DER
A• DERA facets have explicit semantics
and are modeled as descriptive ontologies
• DERA facets inherits all the nice properties of the faceted approach, such as robustness and scalability
• DERA allows for a very expressive document search by any entity property
• DERA allows for automated reasoning via the formalization into Description Logics ontologies
ISKO UK11/26/2013
16
Concl
usi
ons
The usefulness of moving from KO to KR• KO is methodologically very strong, but
subjects are limited in formality and expressiveness as, by employing classification ontologies, it only supports queries by document properties.
• KR, by employing descriptive ontologies, supports queries by any entity property, but it is methodologically weaker than KO.
We propose the DERA faceted KR approach• DERA, being faceted, allows the development of
high quality and scalable descriptive ontologies• DERA, being a KR approach, allows modeling
relevant entities of the domain and their E/R/A properties and enables automated reasoning.
• It supports a highly expressive search of documents exploiting entity properties.
ISKO UK11/26/2013
17Fr
om
KO
to K
RThank
you!