A SEMANTIC SEARCH AGENT APPROACH: Finding Appropriate Semantic Web Services based on User Request Term(s)

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21 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

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1


A SEMANTIC SEARCH AGENT APPROACH:

Find
ing Appropriate Semantic Web Services based
on User Request Term(s)




Duygu Çelik
1

and Atilla Elçi
2

Computer Engineering Department, Eastern Mediterranean University,
M
a
gusa, Mersin
-
10, Turkey

1
duygu.celik@emu.edu.tr

2
atilla.elci@emu.edu.tr






Abstract


This paper proposes a searching mechanism to discover
Semantic Web Services satisfying user requirements. The increas
e in web
services and lack of semantic base in search mechanisms of UDDI make it
difficult for users to find a required web service. It is proposed to develop
a Semantic Search Agent (SSA) to discover required web services from
web. The system uses OWL
-
S f
or describing semantics of web services
and discovers suitable semantic web services through these semantic
descriptions. OWL
-
S allows semantic description of web services and
hence, the Semantic Search Agent is able to understand predefined
concepts of Se
mantic Web Services, extract necessary information and
decide on the requirement of a service for a user. The Semantic Search
Agent interacts with user and Semantic Web Services. The system
propose
s

to combine

complimentary
aspects of two research topics
(
Smart Web Query Engine and Matchmaking Algorithm of OWL
-
S/UDDI Matchmaker) to facilitate the provision of web services to a user.



Keywords
-

Semantic Search Agent
,

SSA, Semantic Web Services,
OWL
-
S,
Smart Web Query,

Matching Algorithm.



I. INTRODUCTION


Currently, web services interact by passing XML data, with data types
specified using XML Schema. Simple Object Access Protocol [SOAP] can be
used as the communication protocol [1], and the I/O signatures for web

2

services are given by Web Services Descript
ion Language [WSDL] [2].
UDDI stands for Universal Description, Discovery and Integration [3] and
provides the means to publish and discover
web services through a UDDI
registry.
There are some difficulties in discovering required web services by a
user du
e to increase of web services and lack of semantic parts in current web
service technologies such as UDDI and WSDL. Due to this fact, the web
requires an intelligent system, which
may

be
implemented
using Semantic
Web. The lack of semantic parts in web ser
vices technologies and also
increasing number of web services are making it too difficult for finding
suitable web services according to the user’s request.

The topic of this article is a discussion of the development of a
Semantic Search Agent (SSA) that

understands ontologies, concepts of
Semantic Web Services and connects to each of these services. The SSA
extracts necessary information choosing Semantic Web Services (SWS) that
are suitable for the user based on user’s request. The proposed SSA is used

to
service the user by looking at the semantic description of the SWS. It presents
semantically searched results to the user detailing the services selected based
on the request received by SSA. First, the agent enhances the user input term
with
synonym

o
r some related words such as
is_a
, then it checks if there is a
match between user’s request and the profile information (ontologies) of
SWS. The proposed system helps to find suitable SWS that provide service to
users. The proposed system includes a
n

SSA
placed between user an
d SWS.
The SSA

provides communication between users and SWS; therefore,
i
t
should be able to understand the semantics of SWS and communicate the
user’s semantic information to SWS and vice versa. The agent should decide
and suggest if

a particular service is useful, according to a user’s requ
est, as
an SSA

for finding suitable
SWS
for users. Based on the aforementioned
scenario we classify the S
SA

work in two main parts: Semantic Enhancement
of Input Terms and Matchmaking Algorithm.

T
he article is arranged as follows: section 2 discusses
the
proposed
system and its methods
; it further

provides a survey on smart search engines
and UDDI search mechanisms. Section 3 details the process structure of the
system developed during this researc
h. The architecture focuses on two main
functions of our proposed
SSA
, namely


Semantic Enhancement of Input
Terms


and

Matchmaking


s
tep
s
. Section 4 presents a case study of the
proposed system and finally, the last part is the conclusion to this articl
e.




II. PROPOSED SYSTEM AND APPLIED METHODS


We classify the
SSA

work in two main parts: Semantic Enhancement of
Input Terms and Matchmaking Algorithm.


3

The proposed system further combines aspects of two research topics
(Smart Web Query Engine and Match
making Algorithm of OWL
-
S/UDDI
Matchmaker) to facilitate the finding of semantic web services for a user.
First, the
SSA

takes user

s request word(s) and enhances the
m
with
their

synonym

and
is_a

related terms. Then all terms are sent to UDDI
registry
serv
er to

retriev
e

all
related web services
.


Afterwards the agent connects to all of the related web services’ OWL
-
S
files and extracts the necessary input/output information about them. The
SSA

compares

the input/output information of the user
against that o
f the

SWS
(profile information in semantic description of SWS) based on the
matchmaking algorithm.



II.I. Smart Web Query Engine


Smart Web Query Engine [5] discusses search engines. Search engines
are very important tools for the people to get informatio
n from Internet but th
e
low
-
accuracy and low
-
recall pers
ists widely in
current search engines. Search
e
ngines provide only primitive data query capabilities and require a detailed
syntactic specification to retrieve relevant data.

Smart Web Query Engine p
roject [5] investigates a new web query
method that is called Smart Web Query (SWQ) method for semantic retrieval
of web data. The SWQ method uses domain semantics represented as context
ontologies. These context ontologies are specified and formulate suit
able web
queries to search.

A user
supplies

some keywords that are separated
by

Boolean operators
and some search parameters such as maximum number of pages to consider
for search operation.
P
rocessing this input the engine bu
ilds a parse tree for
the user
’s

query. User keyword
s are

enhance
d

with synonym, antonym and
other

relat
ed

terms such as “is a” or “act
s on”; the engine prepares a

suitable
query which is sent to some classical search engine(s) to retrieve relevant
pages. Next, the engine applies some
semantic search filters to returned
relevant pages.

Th
e present
study found this approach to be helpful
;

hence some
concepts of SWQ to retrieve relevant web services from UDDI

are used here
.
Th
us th
e first task of
an

SSA
, proposed in this article,
is to
p
erform SWQ
logic for Semantic Enhancement of Input Terms.



II.II. Matchmaking Algorithm



4

UDDI is a standard for discovery of Web Services that stands for
Universal Description, Discovery and Integration. The UDDI Project is an
industry plan that is workin
g to enable businesses to quickly, easily, and
dynamically find and carry out transactions between each other.

Nowadays, many researchers are targeting on adding semantics to
UDDI for the purpose of enhancing the discovery mechanism of UDDI. One
such stud
y, developed by Paolucci et al, is called OWL
-
S/UDDI Matchmaker
[6,7] that describes an algorithm for matching service advertisement and
service request. Paolucci et al developed a Matching Engine that performs
flexible matches
recognizing

the
similarity

o
f an advertisement in UDDI and a
user request. Matchmaking Algorithm is the algorithm implemented in UDDI
by Paolucci et al [6,7]. UDDI records advertisements of services and supports
search of services that provide a set of requested functionalities.
Howe
ver
UDDI
, i
n its current standard
, does not provide any support for finding
services on the basis
semantics
. Advertisement and user request
on the other
hand
refer to OWL concepts and the associated semantic
s
. The
Matchmaking
Algorithm t
ries to recognize s
emantic matches
between all advertisements and
a user request
despite their syntactic differences and difference in modeling
abstraction. No matching occurs if a request does not recognize any
advertisement. A match is found if and only if for each output

of the request,
there is
a
matching output in the advertisement. Degree of success depends on
the degree of matche
s

detected. If one of
the
request’s output is not matched
by any of advertisement's output then the match fails

(Figure
1
).

The matching
even
ts
have same structure for inputs
.




Figure
1
. Algorithm for Output Matching [6].


Figure
2

shows the rules

for degree of match
,
namely,

exact, plug in,
subsume and fail. OutR symbolizes one output of the request whereas OutA
s
ymbolizes output of the advertisement. If OutR and OutA are same or if
OutR is an immediate subclass of OutA then, degree of match will be “exact”.



5



Figure
2
.

Rules for Degree of Match Assignment [6].


Finally, our purpose is

to

implement that algorithm on our
SSA

instead
of
at the
UDDI

side
. UDDI will work classically in our study. The agent will
perform the matching algorithm between the user inputs/outputs and
those of
the
SWS. The proposed system is able to specify relatio
nships between those
inputs/outputs parameters such as EXACT, PLUGIN, SUBSUME and FAIL.
According to those relationships the system serves suitable web services to the
user based on user request. If the agent will find any similarity according to
above rul
es then it will decide
on usefulness of
that web service. After that all
elimination of the returned web services by the UDDI, the agent will prepare
an interface and send it to user as a result.



III. ARCHITECTURE OF

THE SEMANTIC SEARCH
AGENT


The purpos
e of this system is to guide a user looking for a suitable web
service. This is achieved by using
SSA

to locate
SW
S according to user’s
request.
SSA

understands this request in an a
ppropriate manner and
tries to
find suitable SWS through their ontologies/c
oncepts. Thus, users can find
required web services in efficient way
s

and with ease
of
matching their
requirements. The agent interacts with user and SWS from the beginning of
the process till the end. Next subsection discusses a detailed process structure

of the proposed
SSA
.



III.I. A Process Diagram for the Semantic Search Agent


Web Service technologies such as UDDI and Semantic Web
technologies such as OWL
-
S are used to develop this project. OWL
-
S is used
to add semantic
s

into web services and the SSA

is able to understand meanin
g
of SWS contents render
ing it able to use the full potential of SWS. The
response generated by the SSA will then provide references
t
o selected
services.
Semantic information about services such as input, output, and
precondit
ion is stored in the profile of the web services.
The following

figure
gives brief information about system architecture, which includes the us
er, the

6

UDDI registry,
related SWS and their OWLS files, predefined ontologies and
their databases and lastly
th
e
proposed
SSA
.
F
igure
3

shows details of our
system process structure. First of all, user enters request word as an input to
the system. Then, the SSA takes entered word from user. Next, the agent
check
s

all ontologies
against the

entered word and prepare
s

an interface to ask
the user which ontology is related with its search. The user selects the relative
ontology name and sends it to agent. The agent then looks for
synonym/relational words which are stored in selected ontology by the user
and enhances th
e entered word with synonyms thus the search operation
makes UDDI more accurate. The agent has two important roles/tasks, which
are Semantic Enhancement of Input Terms and Matchmaking Step. All
operations at this point are enhanced by the agent input using

the input term
and synonym terms or some relational terms.

An important point is that the entered term by the user is enhanced with
synonym terms and is_a rela
ted terms. In this project, sample

ontologies were
prepared for defining relations between term
s
;

SSA is to find appropriate
ontology according to the user request and then operates on that ontology to
extract the terms similar to the term entered by the user. The prepared request
is sent to the UDDI for retrieving

relevant web services. For example
, if we
send the “bond” term to UDDI directly, the UDDI may send back irrelevant
services or is unable to find any services reco
r
ded as “bond” in UDDI. So, the
system should first find terms related to the term “bond” and then send the
service request to U
DDI.




7



Figure

3
.

A Process Chart of the Semantic Search Agent


Remaining

operations of the agent are similar to that of matchm
aker
engine project (depicted by

10

through 13,

see Figure
3
). The UDDI returns
related service names and address information s
ent to agent through user.
Now, the agent starts to perform matchmaking step and it connects
to
those
returned services and extracts I/O information
from their
profile
s

based on
their OWL
-
S ontologies. The user input information and output information
will

be compared with those
of the
services. It was necessary to have an
efficient algorithm here and matchmaker algorithm was found to be best
suited for this purpose [6, 7, 8 and 9]. After performing matchmaki
ng step, the
agent will prepare

an interface
also

including eliminated SWS names and
addresses then sends it to user as an output at step 14
(
see Figure
3
)
.

As
mentioned, there are two main roles in
SSA
’s tasks list. This section
gave brief description of the system architecture and the tasks of the mai
n
procedures in t
he system. Next sections detail

information on how Semantic
Enhancement of User Input Terms and Matchmaking Step are applied to the
system.



IV. A CASE STUDY FOR

THE SSA



8


In this section, the
user
’s

request for documents on “bond” is ta
ken
into consideration in the following
;

semantic enhancement and matchmaking
steps are carried out in providing the user with proper semantic information.



IV.I. Semantic Enhancement of User Input Terms


In this study, the step for semantic e
nhancement o
f user

s input terms
performs enhancement of the terms entered by the user with its synonym(s).
As the web services search for the terms
,

results become more and more
accurate. As a result the user retrieves all services, related to its request,
available
in UDDI. UDDI can do only syntactic based search operation so
couldn’t find related web services in its registries.
The user enters some
term(s), using these term(s) system tries to find ontologies that include these
terms.



Figure
4
.

A Part of the Financial Investment Ontology.


The user should select most relevant ontology to service search
operation. In order to help ontology determination, the engine matches the
user’s keywords to the ontologies and present
s appropriate ontologies. For
example “Bond” terms can be found in both financial trading and adhesives
ontologies. At this point, the system uses the user to determine which
ontology is most relevant to its required service. Some synonym keywords
and some

relational keywords, such as “is a”, are produced according to the
terms entered by the user. The terms entered by the user and their synonyms
as well as the related terms are passed to UDDI search registries.

Figure
4

illustrates part of financial invest
ment ontology and underlines
how relation terms of financial investment ontology are developed. The
system uses this ontology as it makes it more understandable and describes
behavior of the system. During the process of this research some

ontologies

9

were
created using Protégé

3.0 and some queries were applied to these
ontologies

using Algernon, a plug
-
in for Protégé

3.0.

For example, if the user input is the "bond" and SSA wants to check
which ontologies contain the term “bond” in them, furthermore the SS
A
wishes to display these results to the user for the purpose of reducing the
search area then it has to follow the routine below.


( (:Instance source_terms ?a)


(term ?a ?b)


(:FAIL(:NEQ ?b "bond"))


(relationship_code ?a ?c)


(:Instance relationshi
ps ?d)


(relationship_code ?d ?e)


(:FAIL(:NEQ ?e ?c))


(ontology_name ?d ?f) )



Figure
5
.

Query finding the ontologies containing the term “Bond”.


As shown in “f” block in Figure
6
, the term “Bond” can appear in two
different ontologies, Financial I
nvestment Ontology and Adhesives Ontology.
After that the system will show the names of those ontologies and the system
will ask to user which ontology is relevant with your search. After that, for
example the user chooses the financial investment ontology
. Anymore the
system will operate on only that ontology.



Figure
6
. Result of the “Bond” term includes by above ontologies.

Finally
, the agent is checking the "bond" term in financial investment
ontology for finding its synonym terms that is shown as a q
uery in Figure
7
.


( (:Instance source_terms ?a)

(term ?a ?b)

(:FAIL(:NEQ ?b "bond"))

(relationship_code ?a ?c)


10

(:Instance destination_terms ?d)

(relationship_code ?d ?e)

(:FAIL(:NEQ ?c ?e))

(term ?d ?f)

(:Instance relationship_properties ?g)

(relationship
_code ?g ?h)

(:FAIL(:NEQ ?h ?e))

(relationship_property_name ?g ?i)

(relationship_property_value ?g ?l) )


Figure
7
.

Query finding the synonyms of the term “Share”.


The system enters in selected ontology (Financial Investment) by the
user and searches th
e term "bond" synonyms and is_a relations. The system
recognizes the term “bond” has two synonym terms that are "stock" and
"share". Also the SSA checks is_a terms for the term “bond” that found one
is_a relation such as “instrument” according to financial

investme
nt ontology
(Please see Figure 4
).

After specifying the synonyms and is_a relational terms, the SSA
prepares to enhance search criteria and begins searching in UDDI for related
web services. The system applies matchmaking step after searching UD
DI.
This matchmaking is done for all returned web services related to the user’s
criteria. The next section describes how the system applies matchmaking step.



IV.II. Matchmaking Step



SWS’ profile(s) stores IOPE (Input, Output, Precondition and Effect)
information. The Input of the user information and that of the UDDI provided
SWS profile(s) as well as the output of the user information and the output of
the UDDI provided SWS profile(s) are matched to each other, making the task
of matchmaking logical a
nd easy to implement. The system extracts the
respective IOPE and attempts to apply matchmaking step in order to find the
degree of a match between user request and UDDI provided SWS profile(s).

Figure 9 shows a simple example illustrating how a user requ
est for a
service is matched with a financial investment of SWS. In this figure the
service gives price information of financial investments. The input of the
service is restricted by instances of the concept Price as defined in Fin_Inv
ontology and the se
rvice generates outputs as instance of concept of financial
investments in Fin_Inv ontology.


<profile:Profile rdf:ID="FinancialInvestmentSellingService">


profile:serviceName>StockSellingService</profile:serviceName>


<input>


11




<profile
:ParameterDescription rdf:ID="Price">




<profile:ParameterName>Price</profile:ParameterName>




<profile:restrictedTo rdf:resource="Fin_Inv.owl#Price"/>


</profile:ParameterDescription>


</input>


<output>

<profile:ParameterDescripti
on rdf:ID="Finacial_Investments">




<profile:ParameterName>FinacialInvestments
</profile:ParameterName>

<profile:restrictedTo rdf:resource="Fin_Inv.owl#FinacialInvestments"/>


</profile:ParameterDescription>


</output>

</profile:Profile>


Figu
re
8
.

I/O information of Financial Investment Selling Service


A possible user input is instances of
“Price”

and its output is an
instance of the term
“Bond”
. The matching operation needs a match between
the inputs and outputs information of user and the i
nputs and outputs
information of the above SWS respectively. In this scenario the inputs of the
user and the SWS are same so their match is termed as exact match according
to matchmaking algorithm. Furthermore, the user’s output is the term “Bond”,
whereas

the SWS output is “financial investment”. As the term “bond” is a
subclass of the ontology “financial Investments” so they are classified as an
exact match as well. Finally, the SSA recognizes that the user request and
SWS are related to each other becaus
e exact match was found for both their
inputs and outputs information.



IV.III. Result of Case Study


The case study was performed by system’s processing a user request
and providing the user with SWS that can service that particular request. In
this case

study the user request contained the term “Bond”. The system first
attempted to find ontologies that contain, or are related to, the term “bond”. In
this attempt the system found two ontologies, “Financial Investment”
ontology and “Adhesives” ontology, co
ntaining/related to the requested term
(bond). After discovering that two ontologies for the

specific request the SSA
querie
s the user
on
what ontology interests it. The possible answer “Financial
Investment” is chosen by the user and this causes the SSA t
o investigate all
synonym(s)/related of the requested term “bond” in that ontology. Finally,
having found the related terms and synonyms of the requested term “bond”
,

the SSA looks up the UDDI
server to locate all

matching
web services that
exist in its re
gistry. This is done to increase the probability of finding a

12

suitable service in the UDDI registry for the user’s request. For instance, one
suitable web service found for “bond” term was “Financial Investment Selling
Service”. SSA now matches the input a
nd output parameters of the user to
that of the web services. In this case study
, the degree of match

for the user
requested term “bond” with the SSA discovered service “Financial Investment
Selling Service”, was found to be “exact”. Finally, the system un
derstands
that this service is suitable for the user request.



V. CONCLUSION


The main problems of current web service technologies are the shortage
of semantic parts, increasing number of web services in the web, and
syntactic
-
based search operation f
or web services. These problems make
discovering of appropriate web services challenging.

This article provides an implementation of a
SSA

that can be used to
find related Semantic Web Services

based on a user’s request. The
SSA

(SSA) developed here

perfor
ms two main functions, namely, (i) enhancing
user request term with synonym or related terms such as is_a terms and (ii)
matching the SWS I/O and user I/O. The agent specifies and shows the results
of its findings as a page, including results of related we
b services,
also
showing
the
scores
computed during
the matchmaking step.

The purpose of the matchmaker algorithm

[6, 7, 8 and 9]

was to be
implemented in UDDI. However, UDDI being a well
-
established simple
standard, it should not be subjected to change. H
ence, we propose that the
SSA
approach
should be used for this purpose. The agent can determine the
services appropriate for the user and suggests
them

to the user. The agent also
considers the functionalities of web services and communicates these
functio
nalities/operations to the user. This way the agent performs useful
decision
-
making process for the user.

The proposed system is implemented in the JAVA platform by using
NETBEANS 4.0. Also, the Financial Investments, Vehicle, Software and
Adhesive ontolog
ies are created in
Protégé

3.1. Some semantic web services
are created for our test applications in
Protégé

3.1 as well. The user sends
some request parameters to the system such as the term “Bond” and the
system checks th
is

term in our ontolog
y DB
. Then t
he system sends to the
user all retrieved ontology names that include the term “Bond”. The user
selects the most relevant ontology according to his/her search criteria. The
system works only on
the
selected ontology from then on. The system
accesses the se
lected ontology and extracts all related terms such as synonym
terms and is_a related terms according to the term(s) entered by the user.
Then all terms are sent to the UDDI
server
and the system receives all
semantic web services information and their add
resses. The system asks for

13

input/output parameters from user after that connects to all those returned web
services and extracts profile information by accessing their OWL
-
S file(s) or
their semantic descriptions. Finally, the system starts to perform the

matchmaking algorithm between all input/output parameters of user and
input/output parameters of returned web services. The system is able to
specify some relationships between those input/output parameters such as
EXACT, PLUGIN, SUBSUME and FAIL. Accordi
ng to those relationships
the system serves appropriate web services to the user based on user request.

This
approach m
ay
be applied i
n
numerous domain search
requirements, such as
in

e
-
learning
,
the system can be used for searching,
selecting and register
ing courses/modules. By finding, selecting and
combining these services, the system can decrease the user interaction in these
services for e
-
learning management.

We just used two properties for semantic enhancement of user input
terms. As was mentioned
earlier these properties are synonym and is_a.
Different
properties m
ay be employed
depending
requirements of the interest
domain.

Similarly, by
increas
ing

the number of properties the performance of
the system will improve. Thus, the SSA will be able to g
et better results and
s
elect better web services.



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[11]
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, Last Visited Page: 18/07/2005.


14

[12]
http://www.mindswap.org/2004/owl
-
s/api/index.shtml
, Last Visited Page: 18/07/2005.

[13]
http://java.sun.com/products/jsp/overview.html
, Last Visit
ed Page: 18/07/2005.

[14]
http://protege.stanford.edu/
, Last Visited Page: 18/07/2005.

[15]
http://owlseditor.semwebcentral.org/
, Last Visited Page: 18/07
/2005.

[16] Stefan Tang, a MS Thesis for Matching of Web Service Specifications Using DAML
-
S
Descriptions, March 18th, 2004, Last Visited Page: 18/07/2005,

http://iv
s.tu
-
berlin.de/Projekte/owlsmatcher/resources/thesis_steftang.pdf

,

[17] N. Srinivasan, M. Paolucci, and K. Sycara, "

CODE:
A Development Environment for
OWL
-
S Web services,"
http://iswc20
04.semanticweb.org/demos/10/paper.pdf

,

Last Visited
Page: 18/07/2005.

[18] Anna V.Zhdanova, Jena 2.0 tutorial, Last Visited Page: 18/07/2005,
http://www.deri.at/teaching/semi
nars/internal/slides/Jena
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2.pdf

.


[19] C.K. Olesen, T.Hqjgaard"
, MS thesis is for a Semantic UDDI Repository Combining
OWL
-
S and WSDL
-
S,
Last Visited Page: 15/07/2005,
http://gr
oups.yahoo.com/group/semanticweb/files/
Sem
-
UDDI
.pdf

.

[20] G. Denker and D. Elenius and D. Martin,
OWL
-
S Editor for Creating Semantic Web
Services,

www.iswc2004.semanticweb.org/dem
os/02/paper.pdf
, Last Visited Page: 15/07/2005.