A vast majority of web services exist without explicit associated semantic descriptions. As a result many services that are relevant to a specific user service request may not be considered during service discovery. In this paper, we address the issue of web service discovery given nonexplicit service description semantics that match a specific service request. Our approach to semantic-based web service discovery involves semantic-based service categorization and semantic enhancement of the service request. We propose a solution for achieving functional level service categorization based on an ontology framework. Additionally, we utilize clustering for accurately classifying the web services based on service functionality. The semantic-based categorization is performed offline at the universal description discovery and integration (UDDI). The semantic enhancement of the service request achieves a better matching with relevant services. The service request enhancement involves expansion of additional terms (retrieved from ontology) that are deemed relevant for the requested functionality. An efficient matching of the enhanced service request with the retrieved service descriptions is achieved utilizing Latent Semantic Indexing (LSI). Our experimental results validate the effectiveness and feasibility of the proposed approach.

In an emergency situation failure to respond in a timely manner poses a significant threat. Data needed for timely response comes from various sources and sensors. These individual data streams when viewed in isolation may appear irrelevant, however, when analyzed collectively may identify potential threats. An effective and timely response also requires collaboration and information sharing among various government agencies at all levels. This collaboration information sharing among agencies can be achieved using service-oriented architecture, where agencies provide access to their information resources and applications using Web services. Each of these agencies has its own rules/policies for providing their services. It is therefore, important to verify the correctness of the emergency response processes with respect to the rules/policies of the collaborating agencies involved in the execution of such processes. In this paper we present an approach which addresses the above challenges. Specifically, the proposed approach: a) employs multi stream data mining for identification of potential threats and disambiguation of alarms; b) provides a methodology for the discovery and selection of relevant Web services; c) employs a timed automata based verification methodology for determining the correctness of emergency response processes with respect to the rules of the collaborating agencies. We provide an overview of the initial implementation of the proposed approach.

Semantic Web technology is a promising first step for automated Web service discovery. Most current approaches for Web service discovery cater to semantic Web services, i.e., Web services that have associated semantic descriptions. It is unrealistic, however, to expect all new services to have associated semantic descriptions. Furthermore, the descriptions of the vast majority of already existing services do not have explicitly associated semantics. In this paper we present a novel approach for Web service discovery that combines semantic and statistical association metrics. Semantic metrics are based on the semantic aspects of relevant ontology. Statistical association metrics are based on the association aspects of Web services instances (their inputs and outputs). Specifically, our approach exploits semantic relationship ranking for establishing semantic relevance, and a hyperclique pattern discovery method for grouping Web service parameters into meaningful associations. These associations combined by the semantic relevance are then leveraged to discover and rank Web services