ITK 478 Advanced database Position Paper - Relational.OWL Vs Ontology-based Framework for Integrating Databases into the Semantic Web

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ITK 478 Advanced database


Position Paper
-


Relational.OWL Vs Ontology
-
based Framework for
Integrating Databases into the
Semantic Web





































Submitted By


K.Venkat



ITK 478 Advanced database


Position Paper




2

Relational.OWL Vs Ontology
-
based Framework for
In
tegrating Databases into the Semantic Web


1.
Introduction:


Semantic web is a new web technology
allows t
he data to be shared among different data source.

Semantic
web integrates data from different data sources, giving flexibility to the application

[6]
. Web ontology
language and resource description framework are the two technologies that are used for representing the
semantic web data, these languages are recommended by the World Wide Web (W3C). Ontology language
is used for storing information
that co
mbines the
domain or communities and

Resource framework
description

is a standard for storing

information

that merge different data source

[3]
.

Most of the data
accessed from different data source, is stored by and large in relation database so there is ne
ed for
transforming the relational database to the semantic data.
Mapping the queried executed data from the
relational data source and the semantic data is not reliable because
relationship defined in the relational
data schema is different from the seman
tic data.


Relational.
Owl is a technique that represents

the relational schema
in

the ontology

language, which can be
used by the semantic web. Querying the converted schema is done through
SPARQL.

It is a
query lan
guage
used for q
uerying different data s
ources.
With this approach different relational data source can be
represented in ontology automatically and can be queried using SPARQL. Section 2 discusses briefly on
using SPARQL and Relational.Owl for representing relational database.


The al
ternat
ive to the above alternative

is ontology based
framework, web PDDL
, a first order ontology
language is used for representing the mapping, structure and semantics of the relational data and

Onto
grate
Engine for querying

[1]
.

The working and more information

on Ontograte is disused in section 3.


From the positives and negatives of the above

approaches, position
is

taken based on the performance and
capability.



2.
Relational.Owl and SPARQL:


In this approach,
first the
data from the relational database is

represented in
ontology languages which are
understood by semantic applications. Relational.Owl is used for representing relat
ional schema in ontology
language.
Relational.Owl has different classes defined for example table, column, database, etc
.

The dat
a
from
any
data source

can be

represented as instance of these classes.
It also defines the relationships of the
classes are defined from the original database schema.
Data represented in other ontology language or RFD
can be mapped to this new schema usin
g syntax such as owl:equivalentClass or owl:equivalentproperty.

So
using Relational.Owl we have transferred the data form different data source to a common ontology
language. The querying of the data is now done using the languages such as SPARQL, RDQL and

RQL

[2]
.

The figure 1 shows example
of Relational.OWL

ontology.



Figure 1: Relational.owl ontology
and Schema representation
[7]

[2]


ITK 478 Advanced database


Position Paper




3

SPARQL performs
most
of the

basic operations that can be performed using the SQL. The general syntax
of SPARQL is sh
own below

[
5
]
.


SELECT ?
Title

WHERE

{

<http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> ?title

}


The output for the above query
is:



The tags refers to the class book and title which is equal to the book table and title col
umn i
n the relational
database and ?name represents the variable that are to be shown in output.

The join operation in the
SPARQL is shown below

[2]
. The term prefix
with a keyword
can be used for giving a
namespace
for the
table,
columns

and relations
.



PREFI
X rdf:[...]

PREFIX db :[...]

CONSTRUCT
{
?a ?b ?c;

?e ?f
}
WHERE
{{
?a ?b ?c;

rdf:type db:COUNTRY
}
.

{
?d ?e ?f;

rdf:type db:ADDRESS
}
.

{
?a db:COUNTRY.COUNTRYID ?x
}
.

{
?d db:ADDRESS.COUNTRYID ?x
}}


With the use of SPQRL and
Relational.Owl

we can use the relati
onal data in the semantic web applications.


3.
Ontology based frame

work:


In this approach the data is represented in the ontology through Web
-
PDDL and is given to the Ontograte
for the data querying and
translation. The data form the relational table ar
e converted to ontology langu
age
using Web
-
PDDL, here inheritance, namespaces, type, predicates, axioms, functions and facts are used to
data schema is represented in ontology. For example Inheritance is used for getting aggregation, relations
and data typ
es in the relational schema and axioms gets the relationship between the tables.

The ontology
developed is given to the Ontograte engine for data integration.
Ontograte engine integrates data
that is
represented in Web
-
PDDL
.


The data is converted in to on
tology language and relationship is defined between the two data and then
querying the required data is done.
The Web
-
PDDL is converted to OWL first using the PDDOWL and
then used by the semantic web application
.

We use the same process for already existin
g ontology, except
for the translation of data to ontology language. H
ere we still using mapping technique for relating data
.


The semantic web application can talk query the database using OWL
-
QL query language, this query is
converted to PDDSQL using PDD
OWL, which is in turn converted into the SQL Queries that can query the
database with the help of PDDSQL translators.

The Ontograte architecture
consists

of
following
different
blocks
[1]
.


Integration of schemas and
ontology
: this module converts

the sche
ma into the
ontology, based on the
standards for translation. Complex database conversion is done through both theoretically and
automatically
.


ITK 478 Advanced database


Position Paper




4

Matching generation
:
this module
m
atches helps in matching the
data in correspondence to th
e given data
through

ontology information of the data schema.

Matching is done more precisely
with the help of
learning mapping from the knowledge module and mining large data sets to find candidate mappings

and
these modules are repeated if necessary
.


Learning mapping form
the knowledge
module
:

helps user to define the spe
cific association or case

of
the data

to the system to understand the relation.

M
ining large data sets this module helps the user to associate the data through association rule mining
technique, where the s
imilarities between the databases are taken in consideration for defining the
relationship.



User interface
: this user interface helps the user to giving his inputs for all the above modules



Inference engine
: this module helps in applying all the above
defined mapping rules for querying the data.





4.
Comparing Relational.Owl & SPARQL and
Ontology based frame

work:


Integration:
The
first approach, Relational.Owl

simply transfer a particular database source into ontology
language using the defined cla
sses known as Relational.OWL, it doesn’t
concentrates on
mapping the
relation between the two data sources. Ontology based framework also take care of mapping the data
sources with the help of domain expert.



New query language:
Ontology based fram
e work

use the SQL for
querying
the database. It converts the
SQL queries to OWL
-
QL an ontology based query language using PDDOWL and PDDSQL translators.

SPARQL is still in early stages and still plenty of research is going on.



SPARQL query language used in th
e first approach is relative new query language and it doesn’t fully
support all the functions

such as grouping,

sub Queries etc

of SQL

[2]
.



SPARQL is a semi structured and does not require joints because mapping can be done using relationship
between th
e data that is represented in RDF format It also does not support hierarchal queries
directly
[8].


SPARQL works on RDF

data

(resource description framework).
It does not need the target source to map
the data, In case of onto grate we need a target data

source to map and this mapping can change when any
of the database is updated, which needs manual update of mapping every time when an update is made

[2]
.


OntoG
rate is integrated system with user interface for user to enter the mapping information to the

system.


The
relational.O
wl created for the data source
i.e.

classes defined may not be equal for all the data sources,
for each data source we may have to define different set of classes, which decreases the performance.



Complex subclasses in data sour
ce will make relational.Owl complex and hard to define.

Integrating of xml
documents to the semantic web
is not

supported using OntoG
rate.


The Relational.Owl and SPARQL don’t need any transferring, as we can query data using SPARQL.
Though we need transfe
r in OntoGrate engine it takes around 3 seconds for 1000 records of data

[1]
, which
is relatively fast.


Position:


The transfer data from relational to the data schema that that can be used and queried by the semantic web
needs to me easier to use, have g
ood performance, and should support wide variety of data sources.
Ontology based frame work OntoGrate engine supports wide variety of data sources. It can transfer
100,000 data records in one minute and supports integration of data sources well when compar
ed
to the
ITK 478 Advanced database


Position Paper




5

other approach

[1]
. The OntoG
rate system has a goo
d user interface and integrated

system
that supports in
build transfer. It can take data input of any form except for xml. More importantly it supports the normal
SQL query language, which is trad
itional and mostly used.

The SPARQL query language is relative new,
though it has some advantages it is better to go for Ontograte which uses SQL

and also it is integrated
framework which is experimented
.
Even though we need target data source and manual u
pdate we can go
for
OntoG
rate engine
. So

OntoGrate can
be used ahead of the Rea
ltional.Owl and SPARQL approach for
getting the data form the relational database and performing the different operations between the two data
sources.


References:


1.

Dejing Dou,

Paea LePendu, Shiwoong Kim and Peishen Qi, “
Integrating Databases into the
Semantic Web through an Ontology
-
based Framework
”,

ICDEW
, p.

54,

Proceedings of the 22nd
International Conference on Data Engineering Workshops (ICDEW'06)
,
Year of
Publication:

200
6
.


2.

Cristian P´erez de Laborda, Stefan Conrad, “
Bringing Relational D
ata into the Semantic Web
using
SPARQL and Relational.OWL
”,

ICDEW
, p. 55,

22nd International Conference on Data
Engineering Workshops (ICDEW'06),

2006.


3.

Janet Daly,


World Wide Web Consor
tium Issues RDF and OWL Recommendations

,
http://www.w3.org/2004/01/sws
-
pressrelease

.


4.

Eric
Prud'hommeaux,

Andy Seaborne,

“SPARQL Query Language for RDF”,
http://www.w3.org/TR/rdf
-
sparql
-
query/
.


5.

Eric Prud'hommeaux,

Andy

Seaborne
,


SPARQL Query Language for RDF
”,
W3C Working
Draft 21 July 2005
,

http://www.w3.org
/TR/2005/WD
-
rdf
-
sparql
-
query
-
20050721/#QueryForms
.


6.

“Semantic Web”
,

http://www.w3.org/2001/sw/
,
Date accessed: 2007
-
09
-
24
.


7.

Csongor Nyulas,

Martin O’Connor and,
Samson Tu, “
Data Master



a Plug
-
in for Importing
Schemas and Data

from R
elational Databases into Protege
,

http://protege.stanford.edu/conference/2007/presentations/10.01_Nyulas.pdf
,
Date accessed: 2007
-
09
-
24
.


8.

Lee

Feigenbaum
,
“SPARQL FAQ”,

http://thefigtrees.net/lee/sw/sparql
-
faq
,
Date accessed: 2007
-
09
-
24
.