Agent-Based Semantic Interoperability of Geo-Services

Agent-Based Semantic Interoperability of Geo-Services

Iftikhar U. Sikder (Cleveland State University, USA) and Santosh K. Misra (Cleveland State University, USA)
DOI: 10.4018/978-1-60566-970-0.ch007
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This chapter proposes a multi-agent based framework that allows multiple data sources and models to be semantically integrated for spatial modeling in business processing. The authros introduce a multiagent system (OSIRIS – Ontology-based Spatial Information and Resource Integration Services) to semantically interoperate complex spatial services and integrate them in a meaningful composition. The advantage of using multi-agent collaboration in OSIRIS is that it obviates the need for end-user analysts to be able to decompose a problem domain to subproblems or to map different models according to what they actually mean. The authors also illustrate a multi-agent interaction scenario for collaborative modeling of spatial applications using the proposed custom feature of OSIRIS using Description Logics. The system illustrates an application of domain ontology of urban environmental hydrology and evaluation of decision maker’s consequences of land use changes. In e-government context, the proposed OSIRIS framework works as semantic layer for one stop geospatial portal.
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Selecting the most appropriate semantic web service is one of the important components of the semantic web service composition process. Most aspects of the Semantic Web Service (SWS) composition process such as automatic discovery, selection, and composition are tightly related to the quality of semantic web services (QoS). QoS can be defined as a part of service description and is an especially important factor for service composition (Zeng et al., 2004). In addition to the QoS, the cognitive parameters of service providers can also prove to be the deciding factors in semantic web service selection and composition. They can be used to decide on a particular SWS to invoke by the user among the numerous services discovered. Various cognitive parameters such as capability, desire, intention, commitment, trust, reputation etc. and a number of QoS parameters such as cost, response time, reliability, accuracy, security feature, execution time, exception handling feature, penalty on breaking service contract etc. have to be considered in service selection. To our knowledge, the issue of service selection based on QoS and cognitive parameters has not been thoroughly addressed in the literature till now. This is primarily due to the complexity of QoS metrics and a lack of formal measurement of cognitive parameters. The work by Ermolayev et al. (2004) has presented a method for selection of service provider agents based on some cognitive parameters. But the agent selection model only considers capability and credibility assessment as the base for agent selection and then performs negotiation with each of the capable agent. But assessing these parameters alone may not result in the selection of the best performing agent.

The proposed Hybrid Selection Model (HSM) for service selection can be easily integrated with Multi-Agent based SWS composition process. HSM performs rating of the agents based on their cognitive as well as QoS parameters. Some of the novel features in the model are: providing the formalization and new normalization procedure for QoS parameters, providing the formalization of cognitive parameters, providing a method for measuring the reputation of agent, and providing a dynamic feedback system affecting the reputation of the selected service provider based on the quality of its present service. In support of this work, an evaluation and experimentation is also presented.

The remainder of the paper is organized as follows. Following the introduction section, section 2 describes some similar works. Section 3 provides a description of the hybrid selection model and the details of QoS and cognitive parameters based rating is provided in section 4 and 5 respectively. Section 6 discusses the evaluation of the presented model and some comparison with existing work. The implementation of a system providing service selection based on the proposed model has been discussed in the Section 7. Section 8 provides the conclusion and future work.

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