In recent years there has been a proliferation of scientific resources available through the Internet including, for example, datasets and computational modelling services.  Scientists are becoming increasingly dependent upon these resources, which are changing the way they conduct their research activities with increasing emphasis on conducting ‘in silico’ experiments as a way to test hypotheses.  Scientific workflow technologies provide researchers with a flexible problem-solving environment by facilitating the creation and execution of experiments from a pool of available services.  This thesis investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies.  It is argued that in order to better characterise such experiments we need to go beyond low-level service composition and execution details by capturing higher-level descriptions of the scientific process.  Current workflow technologies do not incorporate any representation of such experimental constraints and goals, which is referred to in this thesis as scientist’s intent.  This thesis proposes an abstract model of scientific intent based on the concept of an Agent in the Open Provenance Model (OPM) specification.  To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL).  Through the use of social simulation case studies the thesis illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.