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International Journal of Research in Computer Science
eISSN 2249-8265 Volume 2 Issue 3 (2012) pp. 51-56
© White Globe Publications
www.ijorcs.org



                 ONTOLOGY MAPPING FOR DYNAMIC
                   MULTIAGENT ENVIRONMENT
                                    M.Jenifer1, P.S.Balamurugan2, T. Prince3
           1
             Research Scholar, Department of Computer Science, Karpagam University, Coimbatore, India
                                              Email: jenimetil@gmail.com
                  2
                    Head, Department of Computer Science, Karpagam University, Coimbatore, India
                                             Email: balabeme@gmail.com
            3
              Lecturer, Dept. of Computer Science, St. Joseph University in Tanzania, Tanzania, East Africa
                                          Email: prince.thomaskl@gmail.com

Abstract: Ontologies are essential for the realization       because this will assure that more and more people
of the Semantic Web, which in turn relies on the ability     will start using ontologies, which is a precondition for
of systems to identify and exploit relationships that        commercial viability of Semantic Web applications.
exist between and within ontologies. As ontologies can       However, the community expectations are also high
be used to represent different domains, there is a high      when one thinks about the potential use of these
need for efficient ontology matching techniques that         applications.    The vision of the Semantic Web
can allow information to be easily shared between            promises a type of “machine intelligence,” which can
different heterogeneous systems. There are various           support a variety of user tasks like improved search or
systems were proposed recently for ontology mapping.         Question Answering (QA). For such applications,
Ontology mapping is a prerequisite for achieving             researchers have developed a wide variety of building
heterogeneous data integration on the Semantic Web.          blocks that needs to be utilized to achieve wider public
The vision of the Semantic Web implies that a large          acceptance. This is particularly true for ontology
number of ontologies present on the web need to be           mapping [2], which makes it possible to interpret and
aligned before one can make use of them. At the same         align heterogeneous and distributed ontologies on the
time, these ontologies can be used as domain-specific        Semantic Web. However, to simulate “machine
background knowledge by the ontology mapping                 intelligence” for ontology mapping, different
systems to increase the mapping precision. However,          challenges have to be tackled. Consider, for example,
these ontologies can differ in representation, quality,      the difficulty of evaluating ontologies with a large
and size that pose different challenges to ontology          number of concepts. Owing to the size of the
mapping. In this paper, we analyzed the various              vocabulary, a number of domain experts are necessary
challenges of recently introduced Multi-Agent                to evaluate similar concepts in different ontologies.
Ontology Mapping Framework, DSSim and we have                Once each expert has assessed sampled mappings,
integrated an efficient feature called QoS-Web               individual assessments are discussed, and a final
Services Composition with DSSsim. ie we have                 assessment is produced, which reflects a collective
improved this framework with QoS based Service               judgment. This form of collective intelligence can
Compositions Mechanism. From our experimental                emerge from the collaboration and competition of
results, it is established that this developed QoS based     many individuals and is considered to be better at
Web Services Compositions Mechanism for Multiagent           solving problems than those from experts who
Ontology Mapping Framework minimizing uncertain              independently make assessments. This is because
reasoning and improves matching time, which are              these experts combine their knowledge and experience
encouraging results of our proposed work.                    to create a solution rather than relying on a single
                                                             person perspective. Miklos Nagy and Maria Vargas
Keywords: Uncertain reasoning, Multiagent Systems,
                                                             Vera proposed and discussed what problems need to
QoS, Ontology Mapping, Semantic, Web Service
                                                             be addressed before one can achieve such machine
Composition, Web Services.
                                                             intelligence for ontology mapping and they introduced
                  I. INTRODUCTION                            a Multiagent Ontology Mapping Framework (DSSim)
                                                             [3] that addresses these problems. From their results,
   The continuously increasing semantic metadata on          the developed multiagent ontology mapping
the Web will make it possible to develop mature              framework operates effectively in the Semantic Web
Semantic Web applications [1] which have the                 environment. However, the mapping performance
potential to attract commercial players to contribute to     might be improved by modifying the architecture of
this Semantic Web vision. This is an important one           DSSim with Web Service Composition, which will


                                                                              www.ijorcs.org
52                                                                             M.Jenifer, P.S.Balamurugan, T. Prince

maintain the semantic similarity. This will improve            The main objective of the DSSim architecture is to
the performance mapping of DSSim and reducing              be able to use it in different domains for creating
uncertain reasoning.                                       ontology mappings. These domains include QA, Web
                                                           Services, or any application that needs to map database
     II. MULTIAGENT ONTOLOGY MAPPING                       metadata, e.g., Extract, Transform, And Load (ETL)
                 FRAMEWORK                                 tools for Data Warehouses. Therefore, DSSim is not
   For ontology mapping in the context of QA over          designed to have its own user interface but to integrate
heterogeneous sources, Miklos Nagy and Maria               with other systems through well-defined interfaces. In
Vargas Vera proposed a multiagent architecture [4].        the prototype implementation, Miklos Nagy and Maria
The main reason for this is, when a particular domain      Vargas Vera have used the Automated Question
becomes larger and more complex, open, and                 Answering System (AQUA) [5], which is the user
distributed, a set of cooperating agents is necessary to   interface that creates first-order logic (FOL) statements
effectively address the ontology mapping task. In real     based on natural language queries posed by the user.
scenarios, ontology mapping can be carried out on          As a consequence, the inputs and outputs for the
domains with large number of classes and properties.       DSSim system are valid FOL formulas. An overview
Without the multiagent architecture, the response time     of the DSSim system is shown in Figure 1.
of the system can increase exponentially when the
number of concepts to map increases.




                                  Figure 1: Overview of the DSSim Mapping System

   The AQUA system and the answer composition                 the particular sub-query for which the belief
component are described the context of overall                function attains the highest value.
framework. The user poses a Natural Language Query          • Answer composition component retrieves the
to the AQUA system, which converts it into FOL                concrete instances from the external ontologies or
terms. The main components and its functions of the           data sources, which are included in the answer
system are as follows.                                      • Aswer composition component creates an answer
                                                              to the user’s question.
 • Broker agent receives FOL term, decomposes it
   and distributes the subqueries to the mapping              The main novelty of the DSSim’s solution is that
   agents.                                                 the authors Miklos Nagy and Maria Vargas Vera
                                                           approach the ontology mapping problem based on the
 • Mapping agents retrieve subquery class and
                                                           principles of collective intelligence, where each
   property hypernyms from WordNet.
                                                           mapping agent has its own individual belief over the
 • Mapping agents retrieve ontology fragments from
                                                           solution. However, before the final mapping is
   the external ontologies, which are candidate
                                                           proposed, the broker agent creates the result based on a
   mappings to the received sub-queries. Mapping
                                                           consensus between the different mapping agents. This
   agents use WordNet as background knowledge to
                                                           process reflects well how humans reach consensus
   enhance beliefs on the possible meaning of the
                                                           over a difficult issue.
   concepts or properties in the particular context
 • Mapping agents build up coherent beliefs by                        III. SEMANTIC SIMILARITY
   combining all possible beliefs over the similarities
   of the sub-queries and ontology fragments.                 For semantic similarity between concepts, relations,
   Mapping agents utilize both syntactic and semantic      and properties, Miklos Nagy and Maria Vargas Vera
   similarity algorithms and build beliefs over the        have used graph-based techniques. DSSim takes the
   correctness of the mapping.                             extended query and the ontology input as labeled
 • Broker agent passes the possible mappings into the      graphs. The semantic matching is viewed as graph like
   answer composition component that corresponds to        structures containing terms and their interrelationships.


                                                                            www.ijorcs.org
Ontology Mapping for Dynamic Multiagent Environment                                                                      53

The similarity comparison between a pair of nodes             all four tracks. Thus, it is not possible to determine
from two ontologies is based on the analysis of their         which participant has the best performance over all the
positions within the graphs. The basic assumption is          datasets provided.
that, if two nodes from two ontologies are similar, their        Nevertheless, in this work, from our analysis report,
neighbors might also be somehow similar.                      the following four systems namely Lily, SAMBO,
   Miklos Nagy and Maria Vargas have considered               RiMOM, and DSSim have shown very good
semantic similarity between nodes of the graphs based         performance in the benchmark, anatomy, FAO,
on similarity of leaf nodes, which represent properties.      Directory, and VLCR datasets, which is shown in the
That is, two non-leaf schema elements are                     Figure. 2.
semantically similar if their leaf sets are highly similar,
even if their immediate children are not. The main
reason why semantic heterogeneity occurs in the
different ontology structures is because different
institutions develop their data sets individually, and as
a result, these metadata sets contain many overlapping
concepts. Assessing the aforementioned similarities in
DSSim, Miklos Nagy and Maria Vargas Vera have
adapted and extended the SimilarityBase and                     Figure 2: Test sets (OAEI 2008) with different Systems
SimilarityTop algorithms [6] used in the current
AQUA system for multiple ontologies. Miklos Nagy
and Maria Vargas Vera aim was that the similarity                 From our literature survey, we have noted that
algorithms (experts in terms of evidence theory) could        DSSim is performing well for semantic similarity. But
mimic the way a human designer would describe a               still its performance might be improved interms of
domain, based on a well-established dictionary. What          matching time and uncertain reasoning, i.e. the
also needs to be considered is, when two graph                performance of this DSSim system could be improved
structures are obtained from both the user and query          further with the an efficient QoS based Web Services
fragments, the representation of the subset of the            Composition Mechanism (QoS-WSC).
source ontology can be a generalization or                       This modified QoS-WSC based DSSim will support
specialization of specific concepts present in the graph.     for the largest datasets, i.e. the matching time of the
This can be derived from the external source and needs        enhanced QoS-WSC based DSSim will be lesser as
to be handled correctly. DSSim adapts and extends the         compared to the existing DSSim System.
SimilarityBase and SimilarityTop algorithms, which
have been proved effective in the current AQUA                           V.    QOS-WSC BASED DSSIM
system for multiple ontologies.                                   In this section, we would like to discuss the various
                                                              existing Web Services Compositions [7], [8], [9], [10]
                                                              approaches namely Semantic Approach. From an
A. DSSim Procedure                                            initial set of available services, we can define Service
   The procedure of the DSSim Mapping System is               Composition as follows.
demonstrated as follows.
 1. List all papers with keywords uncertain and               Definition 1 (Web Service Composition) : Web
    ontology mapping                                          Service Composition aims at selecting and
    a. List all the papers and Keywords                       interconnecting Web Services provided by different
    b. Decompose the given keywords interms of                partners in order to achieve a particular goal.
       Keyword, Publication, and URLs                         Automating Web Service Composition aims to
    c. Communicate to WordNet for Mapping and to              overcome the problem where no single service can
       find ontologies                                        satisfy the goal specified by the service consumer.
    d. Iterate the Mapping procedure and find the
       evidence                                               A. Web Service Composition
 2. Compare the Evidence with different publications             A web service is a software system identified by a
    to Agent 1, Agent 2 and etc.                              URL, whose public interfaces and bindings are defined
    a. Finally send to Broker Agent as Answer                 and described using XML. Its definition can be
 3. Send this Answer to requested User                        discovered by other software systems. These systems
                                                              may then interact with the web service in a manner
IV. EXISTING ISSUES OF DSSIM FOR LARGER                       prescribed by its definition, using XML-based
                 DATASETS                                     messages conveyed by internet protocols.         This
There are thirteen participants took part in the OAEI         definition has been published by the World Wide Web
2008 campaign, but only one of them was present in            consortium W3C, in the Web Services Architecture


                                                                               www.ijorcs.org
54                                                                             M.Jenifer, P.S.Balamurugan, T. Prince

document. The web service model consists of three          invoking a composite service can itself be exposed as a
entities, the Service Provider, The Service Registry       web service.
and the Service Consumer. The Figure 3 shows a
                                                              Since it is a widely used approach to use
graphical representation of the traditional Web Service
                                                           conventional programming languages        to link
Model [7], [8].
                                                           components to a composite web service and thereby
                                                           bridge heterogeneous middleware platforms, it
                                                           becomes necessary to develop a Service Composition
                                                           Middleware to support composition in terms of
                                                           abstractions and infrastructure as well.
                                                           B. Web Service Composition Approach in DSSim
                                                              The general and typical functionalities of Web
                                                           Service Composition is disucssed in the previous
                                                           section. In this section, we have followed the same
                                                           procedure for decomposing key words / publications
                                                           and we have done mapping process with newly created
                                                           and maintained WSC Databases if the contents are
                                                           exists in WSC. Otherwise, the ordinary DSSim
              Figure 3: Web Service Model                  procedure will be followed to match the keywords and
   The Service Provider creates or simply offers the       mapping.
Web Service. The Service Provider needs to describe
                                                              The proposed architecture is shown in the Figure 4.
the web service in a standard format, which in turn is
                                                           As showin the figure, once the user lists the papers and
XML and publish it in a central Service Registry.The
                                                           keywords, this proposed system will refer either the
Service Registry contains additional information about
                                                           request is already approached this matching system or
the Service Provider, such as address and contact of
                                                           not.    If the request is new, the Web Service
the providing company, and technical details about the
                                                           Composition will be created and requested QoS will be
service. The Service Consumer retrieves the
                                                           assigned to WSC. Then the DSSim procedure will be
information from the registry and uses the service
                                                           followed and finally the WSC Database will be
description obtained to bind to and invoke the web
                                                           updated for future use.
service.
                                                              If the request is laready performed one, the
   The appropriate methods are depicted in Figure 4.
                                                           mapping procedure will be executed with the content
by the keywords ‘publish’, ‘bind’, and ‘find’. In order
                                                           of WSC, which will map the ontology with short
to achieve communication among applications running
                                                           period, which will improve the system performance in
on different platforms and written in different
                                                           terms of content matching.
programming languages, standards are needed for each
of these operations.Web Services Architecture is              Further, this proposed mechanism will compare and
loosely coupled, service oriented. The Web Service         monitor the uncertain reasoning with the given
Description Language WSDL uses the XML format to           requested QoS and if the uncertain reasoning level is
describe the methods provided by a web service,            very low as compared with the demanded QoS, the
including input and output parameters, data types and      uncertain reasoning will be removed from the
the transport protocol, which is typically HTTP, to be     database, which is improving the classification and
used. The Universal Description Discovery and              prediction accuracy.
Integration standard UDDI suggests means to publish
details about a Service Provider, the services that are
stored and the opportunity for service consumers to
find service providers and web service details. Besides
UDDI, other standards have been developed as well.
The Simple Object Access Protocol SOAP is used for
XML formatted information exchange among the
entities involved in the web service model.                Figure 4: Overview of the QoS-WSC based DSSim Mapping
                                                           System
   When composing web services, the business logic
of the client is implemented by several services. This
is analogous to workflow management, where the             C. The Procedure of the Proposed QoS-WSC based
application logic is realised by composing autonomous         DSSim
applications. This allows the definition of increasingly
complex applications by progressively aggregating            The procedure of the Proposed QoS-WSC based
components at higher levels of abstraction. A client       DSSim Mapping System is demonstrated as follows.


                                                                           www.ijorcs.org
Ontology Mapping for Dynamic Multiagent Environment                                                              55

Algorithm: Adaptive Web Service Selection
                                                                                   2 * (Pr ecesion * Re call )
                                                                   F − Measure =
    1. If new_query then goto step2 else step3                                       (Pr ecision + Re call )
    2. Initialize Web Service Composition Database
        and required QoS                                   D. Harmonic Mean (H-Mean)
    3. List all papers with keywords uncertain and            Harmonic mean is used to calculate the average of a
        ontology mapping                                   set of numbers. Here the number of elements will be
            a. List all the papers and Keywords            averaged and divided by the sum of the reciprocals of
            b. Decompose the given keywords                the elements. The Harmonic mean is always the lowest
                interms of Keyword, Publication, and       mean.
                URLs                                                                                     
            c. Communicate to WordNet for                                                                
                                                                        H − Mean =                       
                                                                                             N
                Mapping and to find ontologies or                                   1 1    1      1   
                new ontologies                                                       + + + ... +
                                                                                                       
                                                                                                        
                                                                                     a1 a2 a3
                                                                                                  aN   
            d. Iterate the Mapping procedure and
                find the evidence from WSC if exist or
                update WSC                                    From the experimental results, both the DSSim and
    4. Compare the Evidence with different                 QoS-WSC based DSSim are getting almost the same
       publications                                        level of content matching/mapping accuracy, which is
            a. If publications/keywords with WSC           shown in the Fig. 6. But however, this proposed QoS-
                then send Answer                           WSC based DSSim removes more uncertain reasoning
            b. Else compare with the new Agents            keys and the matching speed is more as compared with
                Agent 1, Agent 2 and etc.                  DSSim which is shown in the Figure 5.
            c. Finally send to i. Broker Agent, ii.
                update WSC
    5. Take this answer and compare with Requested
       QoS and send best matching answer to
       requested user


        VI.       EXPERIMENTAL ANALYSIS
   The evaluation uses recall, precision, and F-
measure, which are useful measures that have a fixed
range. They are also meaningful from the mapping
point of view.
A. Precision                                                     Figure 5: Matching Time of QoS-WSC-DSSim
    A measure of the usefulness of a hit list, where hit
list is an ordered list of hits in decreasing order of
relevance to the query and is calculated as

                  {Re levant _ Items}∩ {Retrieved_Items}
   Pr ecision =
                            {Re trieved _ Items}
B. Recall
   A measure of the completeness of the hit list and
shows how well the engine performs in finding
relevant entities and is calculated as
                                                              Figure 6: F-Values of DSSim and QoS-WSC-DSSim

                {Re levant _ Items}∩ {Retrieved_Items}        As shown in the Figure 6, the matching time of
    Re call =
                           {Re levant _ Items}             QoS-WSC-DSSim almost constant for finding more
                                                           key words/publications as compared with DSSim as in
C. F-Measure                                               the case of content available in the Web Services
                                                           Composition. ie for large volume of content matching,
   The weighted harmonic means of precision and
                                                           our proposed work is performing better as compared
recall and is calculated as
                                                           with    the    DSSim        Ontology     System     .


                                                                           www.ijorcs.org
56                                                                  M.Jenifer, P.S.Balamurugan, T. Prince

                VII.    CONCLUSIONS
   In this research work, the recently developed
Multiagent Ontology Mapping Framework, DSSim has
been studied thoroughly and implemented. From our
results, we have observed that the performance of this
work could be improved with QoS based Service
Compositions Mechanism and thus we have developed
QoS-Web Services Compositions based Mechanism
for Multiagent Ontology Mapping Framework. From
our experimental results, it is established that this
developed QoS-Web Services Compositions based
Mechanism for Multiagent Ontology Mapping
Framework minimizing uncertain reasoning and
maximizing content matching/mapping speed, which
are encouraging our proposed work.

                 VIII. REFERENCES

[1] N. Shadbolt, W. Hall, and T. Berners-Lee, “The
     semantic web revisited,” IEEE Intell. Syst., vol. 21, no.
     3, pp. 96–101, Jan./Feb. 2006.
[2] J. Euzenat and P. Shvaiko, Ontology Matching.
     Heidelberg, Germany: Springer-Verlag, 2007.
[3] M. Nagy, M. Vargas-Vera, and E. Motta, “DSSim—
     Managing uncertainty on the semantic web,” in Proc.
     2nd Int. Workshop Ontology Matching, 2007, pp. 156–
     168.
[4] M. Nagy, M. Vargas-Vera, and E. Motta, “Multi-agent
     ontology map-ping framework in the aqua question
     answering system,” inProc. 4th MICAI—Advances in
     Artificial Intelligence, 2005, pp. 70–79.
[5] M. Vargas-Vera and E. Motta, “Aqua—Ontology-based
     question answer-ing system,” in Proc. 3rd MICAI,
     2004, vol. 2972, pp. 468–477.
[6] M. Vargas-Vera and E. Motta, “An ontology-driven
     similarity algorithm,” Knowl. Media Inst., Open Univ.,
     Milton Keynes, U.K., Tech. Rep. KMI-TR-151, 2004.
[7] Zibin Zheng, Hao Ma, Michael R. Lyu and Irwin King,
     “QoS-Aware         Web      Service     Recommendation
     Collaborative Filtering”, IEEE Transactions On Service
     Computing, Vol. 4, No. 2, April-June 2011.
[8] L.-J. Zhang, J. Zhang, and H. Cai, Services Computing.
     Springer and Tsinghua Univ., 2007.
[9] L. Zeng, B. Benatallah, A.H. Ngu, M. Dumas, J.
     Kalagnanam, and H. Chang, “Qos-Aware Middleware
     for Web Services Composition,”IEEE Trans. Software
     Eng., vol. 30, no. 5, pp. 311-327, May2004.
[10] Z. Zheng, H. Ma, M.R. Lyu, and I. King, “Wsrec: A
     CollaborativeFiltering       Based      Web      Service
     Recommender System,” Proc. Seventh Int’l Conf. Web
     Services (ICWS ’09), pp. 437-444, 2009.




                                                                 www.ijorcs.org

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Ontology Mapping for Dynamic Multiagent Environment

  • 1. International Journal of Research in Computer Science eISSN 2249-8265 Volume 2 Issue 3 (2012) pp. 51-56 © White Globe Publications www.ijorcs.org ONTOLOGY MAPPING FOR DYNAMIC MULTIAGENT ENVIRONMENT M.Jenifer1, P.S.Balamurugan2, T. Prince3 1 Research Scholar, Department of Computer Science, Karpagam University, Coimbatore, India Email: jenimetil@gmail.com 2 Head, Department of Computer Science, Karpagam University, Coimbatore, India Email: balabeme@gmail.com 3 Lecturer, Dept. of Computer Science, St. Joseph University in Tanzania, Tanzania, East Africa Email: prince.thomaskl@gmail.com Abstract: Ontologies are essential for the realization because this will assure that more and more people of the Semantic Web, which in turn relies on the ability will start using ontologies, which is a precondition for of systems to identify and exploit relationships that commercial viability of Semantic Web applications. exist between and within ontologies. As ontologies can However, the community expectations are also high be used to represent different domains, there is a high when one thinks about the potential use of these need for efficient ontology matching techniques that applications. The vision of the Semantic Web can allow information to be easily shared between promises a type of “machine intelligence,” which can different heterogeneous systems. There are various support a variety of user tasks like improved search or systems were proposed recently for ontology mapping. Question Answering (QA). For such applications, Ontology mapping is a prerequisite for achieving researchers have developed a wide variety of building heterogeneous data integration on the Semantic Web. blocks that needs to be utilized to achieve wider public The vision of the Semantic Web implies that a large acceptance. This is particularly true for ontology number of ontologies present on the web need to be mapping [2], which makes it possible to interpret and aligned before one can make use of them. At the same align heterogeneous and distributed ontologies on the time, these ontologies can be used as domain-specific Semantic Web. However, to simulate “machine background knowledge by the ontology mapping intelligence” for ontology mapping, different systems to increase the mapping precision. However, challenges have to be tackled. Consider, for example, these ontologies can differ in representation, quality, the difficulty of evaluating ontologies with a large and size that pose different challenges to ontology number of concepts. Owing to the size of the mapping. In this paper, we analyzed the various vocabulary, a number of domain experts are necessary challenges of recently introduced Multi-Agent to evaluate similar concepts in different ontologies. Ontology Mapping Framework, DSSim and we have Once each expert has assessed sampled mappings, integrated an efficient feature called QoS-Web individual assessments are discussed, and a final Services Composition with DSSsim. ie we have assessment is produced, which reflects a collective improved this framework with QoS based Service judgment. This form of collective intelligence can Compositions Mechanism. From our experimental emerge from the collaboration and competition of results, it is established that this developed QoS based many individuals and is considered to be better at Web Services Compositions Mechanism for Multiagent solving problems than those from experts who Ontology Mapping Framework minimizing uncertain independently make assessments. This is because reasoning and improves matching time, which are these experts combine their knowledge and experience encouraging results of our proposed work. to create a solution rather than relying on a single person perspective. Miklos Nagy and Maria Vargas Keywords: Uncertain reasoning, Multiagent Systems, Vera proposed and discussed what problems need to QoS, Ontology Mapping, Semantic, Web Service be addressed before one can achieve such machine Composition, Web Services. intelligence for ontology mapping and they introduced I. INTRODUCTION a Multiagent Ontology Mapping Framework (DSSim) [3] that addresses these problems. From their results, The continuously increasing semantic metadata on the developed multiagent ontology mapping the Web will make it possible to develop mature framework operates effectively in the Semantic Web Semantic Web applications [1] which have the environment. However, the mapping performance potential to attract commercial players to contribute to might be improved by modifying the architecture of this Semantic Web vision. This is an important one DSSim with Web Service Composition, which will www.ijorcs.org
  • 2. 52 M.Jenifer, P.S.Balamurugan, T. Prince maintain the semantic similarity. This will improve The main objective of the DSSim architecture is to the performance mapping of DSSim and reducing be able to use it in different domains for creating uncertain reasoning. ontology mappings. These domains include QA, Web Services, or any application that needs to map database II. MULTIAGENT ONTOLOGY MAPPING metadata, e.g., Extract, Transform, And Load (ETL) FRAMEWORK tools for Data Warehouses. Therefore, DSSim is not For ontology mapping in the context of QA over designed to have its own user interface but to integrate heterogeneous sources, Miklos Nagy and Maria with other systems through well-defined interfaces. In Vargas Vera proposed a multiagent architecture [4]. the prototype implementation, Miklos Nagy and Maria The main reason for this is, when a particular domain Vargas Vera have used the Automated Question becomes larger and more complex, open, and Answering System (AQUA) [5], which is the user distributed, a set of cooperating agents is necessary to interface that creates first-order logic (FOL) statements effectively address the ontology mapping task. In real based on natural language queries posed by the user. scenarios, ontology mapping can be carried out on As a consequence, the inputs and outputs for the domains with large number of classes and properties. DSSim system are valid FOL formulas. An overview Without the multiagent architecture, the response time of the DSSim system is shown in Figure 1. of the system can increase exponentially when the number of concepts to map increases. Figure 1: Overview of the DSSim Mapping System The AQUA system and the answer composition the particular sub-query for which the belief component are described the context of overall function attains the highest value. framework. The user poses a Natural Language Query • Answer composition component retrieves the to the AQUA system, which converts it into FOL concrete instances from the external ontologies or terms. The main components and its functions of the data sources, which are included in the answer system are as follows. • Aswer composition component creates an answer to the user’s question. • Broker agent receives FOL term, decomposes it and distributes the subqueries to the mapping The main novelty of the DSSim’s solution is that agents. the authors Miklos Nagy and Maria Vargas Vera approach the ontology mapping problem based on the • Mapping agents retrieve subquery class and principles of collective intelligence, where each property hypernyms from WordNet. mapping agent has its own individual belief over the • Mapping agents retrieve ontology fragments from solution. However, before the final mapping is the external ontologies, which are candidate proposed, the broker agent creates the result based on a mappings to the received sub-queries. Mapping consensus between the different mapping agents. This agents use WordNet as background knowledge to process reflects well how humans reach consensus enhance beliefs on the possible meaning of the over a difficult issue. concepts or properties in the particular context • Mapping agents build up coherent beliefs by III. SEMANTIC SIMILARITY combining all possible beliefs over the similarities of the sub-queries and ontology fragments. For semantic similarity between concepts, relations, Mapping agents utilize both syntactic and semantic and properties, Miklos Nagy and Maria Vargas Vera similarity algorithms and build beliefs over the have used graph-based techniques. DSSim takes the correctness of the mapping. extended query and the ontology input as labeled • Broker agent passes the possible mappings into the graphs. The semantic matching is viewed as graph like answer composition component that corresponds to structures containing terms and their interrelationships. www.ijorcs.org
  • 3. Ontology Mapping for Dynamic Multiagent Environment 53 The similarity comparison between a pair of nodes all four tracks. Thus, it is not possible to determine from two ontologies is based on the analysis of their which participant has the best performance over all the positions within the graphs. The basic assumption is datasets provided. that, if two nodes from two ontologies are similar, their Nevertheless, in this work, from our analysis report, neighbors might also be somehow similar. the following four systems namely Lily, SAMBO, Miklos Nagy and Maria Vargas have considered RiMOM, and DSSim have shown very good semantic similarity between nodes of the graphs based performance in the benchmark, anatomy, FAO, on similarity of leaf nodes, which represent properties. Directory, and VLCR datasets, which is shown in the That is, two non-leaf schema elements are Figure. 2. semantically similar if their leaf sets are highly similar, even if their immediate children are not. The main reason why semantic heterogeneity occurs in the different ontology structures is because different institutions develop their data sets individually, and as a result, these metadata sets contain many overlapping concepts. Assessing the aforementioned similarities in DSSim, Miklos Nagy and Maria Vargas Vera have adapted and extended the SimilarityBase and Figure 2: Test sets (OAEI 2008) with different Systems SimilarityTop algorithms [6] used in the current AQUA system for multiple ontologies. Miklos Nagy and Maria Vargas Vera aim was that the similarity From our literature survey, we have noted that algorithms (experts in terms of evidence theory) could DSSim is performing well for semantic similarity. But mimic the way a human designer would describe a still its performance might be improved interms of domain, based on a well-established dictionary. What matching time and uncertain reasoning, i.e. the also needs to be considered is, when two graph performance of this DSSim system could be improved structures are obtained from both the user and query further with the an efficient QoS based Web Services fragments, the representation of the subset of the Composition Mechanism (QoS-WSC). source ontology can be a generalization or This modified QoS-WSC based DSSim will support specialization of specific concepts present in the graph. for the largest datasets, i.e. the matching time of the This can be derived from the external source and needs enhanced QoS-WSC based DSSim will be lesser as to be handled correctly. DSSim adapts and extends the compared to the existing DSSim System. SimilarityBase and SimilarityTop algorithms, which have been proved effective in the current AQUA V. QOS-WSC BASED DSSIM system for multiple ontologies. In this section, we would like to discuss the various existing Web Services Compositions [7], [8], [9], [10] approaches namely Semantic Approach. From an A. DSSim Procedure initial set of available services, we can define Service The procedure of the DSSim Mapping System is Composition as follows. demonstrated as follows. 1. List all papers with keywords uncertain and Definition 1 (Web Service Composition) : Web ontology mapping Service Composition aims at selecting and a. List all the papers and Keywords interconnecting Web Services provided by different b. Decompose the given keywords interms of partners in order to achieve a particular goal. Keyword, Publication, and URLs Automating Web Service Composition aims to c. Communicate to WordNet for Mapping and to overcome the problem where no single service can find ontologies satisfy the goal specified by the service consumer. d. Iterate the Mapping procedure and find the evidence A. Web Service Composition 2. Compare the Evidence with different publications A web service is a software system identified by a to Agent 1, Agent 2 and etc. URL, whose public interfaces and bindings are defined a. Finally send to Broker Agent as Answer and described using XML. Its definition can be 3. Send this Answer to requested User discovered by other software systems. These systems may then interact with the web service in a manner IV. EXISTING ISSUES OF DSSIM FOR LARGER prescribed by its definition, using XML-based DATASETS messages conveyed by internet protocols. This There are thirteen participants took part in the OAEI definition has been published by the World Wide Web 2008 campaign, but only one of them was present in consortium W3C, in the Web Services Architecture www.ijorcs.org
  • 4. 54 M.Jenifer, P.S.Balamurugan, T. Prince document. The web service model consists of three invoking a composite service can itself be exposed as a entities, the Service Provider, The Service Registry web service. and the Service Consumer. The Figure 3 shows a Since it is a widely used approach to use graphical representation of the traditional Web Service conventional programming languages to link Model [7], [8]. components to a composite web service and thereby bridge heterogeneous middleware platforms, it becomes necessary to develop a Service Composition Middleware to support composition in terms of abstractions and infrastructure as well. B. Web Service Composition Approach in DSSim The general and typical functionalities of Web Service Composition is disucssed in the previous section. In this section, we have followed the same procedure for decomposing key words / publications and we have done mapping process with newly created and maintained WSC Databases if the contents are exists in WSC. Otherwise, the ordinary DSSim Figure 3: Web Service Model procedure will be followed to match the keywords and The Service Provider creates or simply offers the mapping. Web Service. The Service Provider needs to describe The proposed architecture is shown in the Figure 4. the web service in a standard format, which in turn is As showin the figure, once the user lists the papers and XML and publish it in a central Service Registry.The keywords, this proposed system will refer either the Service Registry contains additional information about request is already approached this matching system or the Service Provider, such as address and contact of not. If the request is new, the Web Service the providing company, and technical details about the Composition will be created and requested QoS will be service. The Service Consumer retrieves the assigned to WSC. Then the DSSim procedure will be information from the registry and uses the service followed and finally the WSC Database will be description obtained to bind to and invoke the web updated for future use. service. If the request is laready performed one, the The appropriate methods are depicted in Figure 4. mapping procedure will be executed with the content by the keywords ‘publish’, ‘bind’, and ‘find’. In order of WSC, which will map the ontology with short to achieve communication among applications running period, which will improve the system performance in on different platforms and written in different terms of content matching. programming languages, standards are needed for each of these operations.Web Services Architecture is Further, this proposed mechanism will compare and loosely coupled, service oriented. The Web Service monitor the uncertain reasoning with the given Description Language WSDL uses the XML format to requested QoS and if the uncertain reasoning level is describe the methods provided by a web service, very low as compared with the demanded QoS, the including input and output parameters, data types and uncertain reasoning will be removed from the the transport protocol, which is typically HTTP, to be database, which is improving the classification and used. The Universal Description Discovery and prediction accuracy. Integration standard UDDI suggests means to publish details about a Service Provider, the services that are stored and the opportunity for service consumers to find service providers and web service details. Besides UDDI, other standards have been developed as well. The Simple Object Access Protocol SOAP is used for XML formatted information exchange among the entities involved in the web service model. Figure 4: Overview of the QoS-WSC based DSSim Mapping System When composing web services, the business logic of the client is implemented by several services. This is analogous to workflow management, where the C. The Procedure of the Proposed QoS-WSC based application logic is realised by composing autonomous DSSim applications. This allows the definition of increasingly complex applications by progressively aggregating The procedure of the Proposed QoS-WSC based components at higher levels of abstraction. A client DSSim Mapping System is demonstrated as follows. www.ijorcs.org
  • 5. Ontology Mapping for Dynamic Multiagent Environment 55 Algorithm: Adaptive Web Service Selection 2 * (Pr ecesion * Re call ) F − Measure = 1. If new_query then goto step2 else step3 (Pr ecision + Re call ) 2. Initialize Web Service Composition Database and required QoS D. Harmonic Mean (H-Mean) 3. List all papers with keywords uncertain and Harmonic mean is used to calculate the average of a ontology mapping set of numbers. Here the number of elements will be a. List all the papers and Keywords averaged and divided by the sum of the reciprocals of b. Decompose the given keywords the elements. The Harmonic mean is always the lowest interms of Keyword, Publication, and mean. URLs   c. Communicate to WordNet for   H − Mean =   N Mapping and to find ontologies or  1 1 1 1  new ontologies   + + + ... +      a1 a2 a3  aN  d. Iterate the Mapping procedure and find the evidence from WSC if exist or update WSC From the experimental results, both the DSSim and 4. Compare the Evidence with different QoS-WSC based DSSim are getting almost the same publications level of content matching/mapping accuracy, which is a. If publications/keywords with WSC shown in the Fig. 6. But however, this proposed QoS- then send Answer WSC based DSSim removes more uncertain reasoning b. Else compare with the new Agents keys and the matching speed is more as compared with Agent 1, Agent 2 and etc. DSSim which is shown in the Figure 5. c. Finally send to i. Broker Agent, ii. update WSC 5. Take this answer and compare with Requested QoS and send best matching answer to requested user VI. EXPERIMENTAL ANALYSIS The evaluation uses recall, precision, and F- measure, which are useful measures that have a fixed range. They are also meaningful from the mapping point of view. A. Precision Figure 5: Matching Time of QoS-WSC-DSSim A measure of the usefulness of a hit list, where hit list is an ordered list of hits in decreasing order of relevance to the query and is calculated as {Re levant _ Items}∩ {Retrieved_Items} Pr ecision = {Re trieved _ Items} B. Recall A measure of the completeness of the hit list and shows how well the engine performs in finding relevant entities and is calculated as Figure 6: F-Values of DSSim and QoS-WSC-DSSim {Re levant _ Items}∩ {Retrieved_Items} As shown in the Figure 6, the matching time of Re call = {Re levant _ Items} QoS-WSC-DSSim almost constant for finding more key words/publications as compared with DSSim as in C. F-Measure the case of content available in the Web Services Composition. ie for large volume of content matching, The weighted harmonic means of precision and our proposed work is performing better as compared recall and is calculated as with the DSSim Ontology System . www.ijorcs.org
  • 6. 56 M.Jenifer, P.S.Balamurugan, T. Prince VII. CONCLUSIONS In this research work, the recently developed Multiagent Ontology Mapping Framework, DSSim has been studied thoroughly and implemented. From our results, we have observed that the performance of this work could be improved with QoS based Service Compositions Mechanism and thus we have developed QoS-Web Services Compositions based Mechanism for Multiagent Ontology Mapping Framework. From our experimental results, it is established that this developed QoS-Web Services Compositions based Mechanism for Multiagent Ontology Mapping Framework minimizing uncertain reasoning and maximizing content matching/mapping speed, which are encouraging our proposed work. VIII. REFERENCES [1] N. Shadbolt, W. Hall, and T. Berners-Lee, “The semantic web revisited,” IEEE Intell. Syst., vol. 21, no. 3, pp. 96–101, Jan./Feb. 2006. [2] J. Euzenat and P. Shvaiko, Ontology Matching. Heidelberg, Germany: Springer-Verlag, 2007. [3] M. Nagy, M. Vargas-Vera, and E. Motta, “DSSim— Managing uncertainty on the semantic web,” in Proc. 2nd Int. Workshop Ontology Matching, 2007, pp. 156– 168. [4] M. Nagy, M. Vargas-Vera, and E. Motta, “Multi-agent ontology map-ping framework in the aqua question answering system,” inProc. 4th MICAI—Advances in Artificial Intelligence, 2005, pp. 70–79. [5] M. Vargas-Vera and E. Motta, “Aqua—Ontology-based question answer-ing system,” in Proc. 3rd MICAI, 2004, vol. 2972, pp. 468–477. [6] M. Vargas-Vera and E. Motta, “An ontology-driven similarity algorithm,” Knowl. Media Inst., Open Univ., Milton Keynes, U.K., Tech. Rep. KMI-TR-151, 2004. [7] Zibin Zheng, Hao Ma, Michael R. Lyu and Irwin King, “QoS-Aware Web Service Recommendation Collaborative Filtering”, IEEE Transactions On Service Computing, Vol. 4, No. 2, April-June 2011. [8] L.-J. Zhang, J. Zhang, and H. Cai, Services Computing. Springer and Tsinghua Univ., 2007. [9] L. Zeng, B. Benatallah, A.H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, “Qos-Aware Middleware for Web Services Composition,”IEEE Trans. Software Eng., vol. 30, no. 5, pp. 311-327, May2004. [10] Z. Zheng, H. Ma, M.R. Lyu, and I. King, “Wsrec: A CollaborativeFiltering Based Web Service Recommender System,” Proc. Seventh Int’l Conf. Web Services (ICWS ’09), pp. 437-444, 2009. www.ijorcs.org