A Synonyms Dictionary Approach in Semantic Web Services Composition
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A Synonyms Dictionary approach in Semantic Web Services Composition Heitor Barros Co-authors: Tarsis Marinho, Evandro Costa, Jonathas Magalhães and Patrick Brito
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In the field of Semantic Web Services, the automatic composition of services has been a challenging research problem. At the same time, mainly due to increasing number of available services and their heterogeneity, the industry has demanded for techniques that automate the process of selection and composition of services. Thus, several techniques for semantic service composition have been developed, but these techniques have shown to considerably hinder the overall performance of the application, making their use very expensive. One of the factors that contribute to this performance problem is the way that planners check the similarity between the ontologies concepts, i.e. the Concepts Matching. Thus, the choice of a proper technique for Concepts Matching directly influences the overall performance of the system and the choice of a proper technique is considered a key design decision in semantic web services. In this context, we propose the use of a Concepts Matching technique based on a Synonyms Dictionary approach aiming to reduce the performance issues found in service composition process. To validate our proposal, we present a systematic experimental analysis which compares this technique with other two approaches. The results show that the Synonyms Dictionary technique has the best performance when compared to other techniques.
Transcript of A Synonyms Dictionary Approach in Semantic Web Services Composition
A Synonyms Dictionary approach in Semantic Web Services Composition
Heitor BarrosCo-authors: Tarsis Marinho, Evandro Costa, Jonathas Magalhães and Patrick Brito
Semantic Web Services
WebService
Semantic Description
Describes
Semantic Web Services
WebService
Semantic Description
Describes
Ontologies
Semantic Web Services
WebService
Semantic Description
DescribesService
DiscoveryTechniqueUses
Semantic Web Services
WebService
Semantic Description
DescribesService
DiscoveryTechniqueUses
ServiceExecution
EngineInvokes
Select a Service
Semantic Web ServicesWeb
Service
Semantic Description
DescribesService
DiscoveryTechniqueUses
ServiceExecution
EngineInvokes
Select a Service
Composition Planner
Uses
Semantic Web ServicesWeb
Service
Semantic Description
DescribesService
DiscoveryTechniqueUses
ServiceExecution
EngineInvokes
Select a Service
Composition Planner
Uses
Service Composition
Plans
Invokes
Semantic Web ServicesWeb
Service
Semantic Description
DescribesService
DiscoveryTechniqueUses
ServiceExecution
EngineInvokes
Select a Service
Composition Planner
Uses
Service Composition
Plans
Invokes
SWS CompositionExample:
Concepts MatchingA Concepts Matching technique checks if two concepts are similar.
In Discovery Process, it checks if service description matches the discovery request.
● Inputs and Outputs parameters, preconditions, effects, service category, etc.
Concepts MatchingIn Composition Process, the Concepts Matching technique also is used to analyze the relationship between services.
Research ProblemProblem: poor performance in the composition process.
Mokhtar et al. (2007), Klusch and Gerber (2006) andTalantikite et al. (2009).
Research Question: How to improve the performance of Composition Process?
Our ProposalThe Concepts Matching technique affects on the performance of services composition.
So, we propose a synonyms dictionary technique to improve the composition process.
Synonyms DictionaryThis technique uses a dictionary structure to keep the information about the concepts similarity.
● For each Concept in the Dictionary there is a set of related concepts that are similar to him.
Synonyms DictionaryDictionary Example:
EvaluationGoals● Check the effectiveness of the proposed technique.● Compare the composition process using the Synonyms
Dictionary with other Concepts Matching techniques.
EvaluationWe chose the following techniques:● Semantic Matching (Paolucci et al., 2002).● Cosine Similarity Measure (Klusch, 2006).● Synonyms Dictionary.
EvaluationWe chose the following techniques:● Semantic Matching (Paolucci et al., 2002).● Cosine Similarity Measure (Klusch, 2006).● Synonyms Dictionary.
○ The dictionary was built using the Semantic Matching technique.
EvaluationWe utilized the OWLS-TC v4 services.
● semwebcentral.org/projects/owls-tc/.
This base has 1083 Semantic Web services written in OWLS 1.1 in 9 different domains (Education, Medicine, Food, Travels, Communications, Economy, Weapons, Geography and Simulation).
Evaluation● We use Grinv Middleware
to make the Composition Process.
● Grinv allowed us to customize the composition techniques (Barros, 2011).
More about Grinv at:github.com/HeitorBarros/Grinv
EvaluationWe have developed three versions of a backward chaining algorithm for composition planning.
Each version has a different Concepts Matching technique.
EvaluationComposition Scenario:
EvaluationComposition Scenario:
● There was only one correct composition.● Every technique was able to find the
correct composition.
We are evaluating performance, not quality.
EvaluationComposition Scenario:
1. Repository with 100 Services.2. Repository with 600 Services.3. Repository with 1000 Services.
For each of these scenarios were performed 10 repetitions.
Results
Results
Results
Results
ConclusionThe experiment shown that the planning of compositions using Synonyms Dictionary had the lowest response time.
The use of Synonyms Dictionary is efficient in automatic composition of services.
The Concepts Matching technique affects on the performance of services composition.
Future WorkImprove Quality:● We will design the integration of other
Concepts Matching techniques with the Dictionary in order to improve the quality of relationships in the dictionary and enable the integration of new ontologies at run time.
References❏ Mokhtar, S. B., Preuveneers, D., Georgantas, N., Issarny, V., & Berbers, Y. (2007). EASY:
Ecient SemAntic Service DiscoverY in Pervasive Computing Environments with QoS and Context Support. Journal of Systems and Software, 81(5), 785–808.
❏ Klusch, M., & Gerber, A. (2006). Evaluation of Service Composition Planning with OWLS-XPlan. In Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (pp. 117–120). Washington, DC, USA: IEEE Computer Society. Retrieved from http://dx.doi.org/10.1109/WI-IATW.2006.68 doi: 10.1109/WI-IATW.2006.68.
❏ Talantikite, H. N., Aissani, D., & Boudjlida, N. (2009, November). Semantic annotations for web services discovery and composition. Comput. Stand. Interfaces, 31, 1108–1117. Retrieved from http://dl.acm.org/citation.cfm?id=1595894.1596056 doi: 10.1016/j.csi.2008.09.041
❏ Paolucci, M., Kawamura, T., Payne, T. R., & Sycara, K. (2002). Semantic Matching of Web Services Capabilities. The Semantic Web - ISWC 2002: First International Semantic Web Conference, Sardinia, Italy, June 9-12, 2002. Proceedings, 333+.
References❏ Klusch, M., Fries, B., & Sycara, K. (2006). Automated semantic web service discovery with
OWLS-MX. In AAMAS ’06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 915–922). New York, NY, USA: ACM. doi: 10.1145/1160633.1160796
❏ Heitor Barros, Alan Silva, Evandro Costa, Ig Ibert Bittencourt, Olavo Holanda, Leandro Sales (2011), Steps, techniques, and technologies for the development of intelligent applications based on Semantic Web Services: A case study in e-learning systems, Engineering Applications of Artificial Intelligence, Volume 24, Issue 8, December 2011, Pages 1355-1367, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2011.05.007.
Thank you!Contact: [email protected]
