International audience

We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.

Contemporary specification languages of workflow management systems focus on capturing process execution semantics. Constructs are offered that allow the specification of sequential execution, iteration, choice, parallelism and synchronisation. While in workflow modelling it is absolutely imperative that exceptions are dealt with properly, virtually no support for the specification of exception handling is offered at the conceptual level. Typically, exceptions and recovery strategies need to be defined using the programming primitives of the specific workflow management systems used. We propose a number of conceptual modelling primitives that can be used for the specification of exception handling in workflows. These primitives are illustrated using some real-life examples. A formal semantics is assigned to precisely define their meaning and demonstrating how they can be incorporated in a typical process modelling language.