Service discovery is one of the key problems that have been widely researched in the area of Service Oriented Architecture (SOA) based systems. Web Service clustering is a technique for efficiently facilitating service discovery. Most Web Service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper, we have proposed a self-organizing based clustering algorithm called Taxonomic clustering for taxonomically organizing semantic Web Service advertisements. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

The World Wide Web (WWW) has become a major platform for hosting, discovering, and composing web services. Web service clustering is a technique for efficiently facilitating web service discovery. Most web service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper we propose a taxonomic clustering algorithm for grouping functionally similar web services. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

Semantic web search involves retrieval of user-specific web artifacts by utilizing their semantic descriptions. A very specific web artifact that has evolved in recent times is web services. Web services can be semantically described in languages like OWL-S. However, such languages are limited with respect to their expressivity of context. They also lack a formal ontological framework where efficient web service retrieval can be conducted. In this paper we model a service request scenario within the web as an event-driven system. Services as well as user requests are modeled as events. We propose an ontological framework called Context-Aware Ontology Framework for Events and Services (CAOFES) where such event-driven web service retrieval can be efficiently executed by a novel reasoning technique.

The three core problems that can be identified with any SOA based system are: (i) service discovery, (ii) service selection, and (iii) service composition. Most of the previous researches have considered these three problems separately. However, we argue that these three problems are intrinsically related and hence, cannot be treated separately. We propose a proactive event-driven model where user activities and services are treated as events. We then graphically model a SOA system into a network of activities called "activity network" (ANet). The nodes of such a network are the services and the edges their mutual causal dependency. We show how both functional as well as contextual information can be incorporated into the network and then justify why that is necessary for solving the three problems mentioned hereby. We then propose an abstraction technique that helps us to simplify the ANet and provide an architectural platform where the three problems can be solved in an efficient manner.

Pervasive Computing plays an integral role in our daily life. As pervasive services become increasingly important to be able to capture context, it is reasonable to speculate that dynamic representations for composition of services is very much needed. Such representations may typically include activities or functional modules that are not only temporal but also responsive to each other. Such actions may be associated with actors who execute them, spatial and temporal information, information-flow and background objects. Consequently, one may model an activity network that depicts causal relations between activities and has the capability of embedding the semantic relations of these activities with other contextual entities. In this paper we discuss a new way of representing such dynamic workflows with a novel data structure called activity logic graphs (ALGs). We introfduce a novel design of an architecture that grasps the semantics of activities (or events) in dynamic pervasive environments, maps that to a collaboration of services (as ALGs) in order to recognize specific situations (known as Situation Boundaries) that has risen out of the events, and implements the recognition process using an efficient indexing technique. We test the model with a set of ALGs and report experimental results on the queries that are essential for service composition.

Today's Web becomes a platform for services to be dynamically interconnected to produce a desired outcome. It is important to formalize the semantics of the contextual elements of web services. In this paper, we propose a novel technique called Semantic Genome Propagation Scheme (SGPS) for measuring similarity between semantic concepts. We show how SGPS is used to compute a multi-dimensional similarity between two services. We evaluate the SGPS similarity measurement in terms of the similarity performance and scalability.