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Monitoring of running scientific workflows (experiments) is not only important for observing their execution status, but also for collecting provenance, improving performance, knowledge extraction, etc. We propose an ontology model of experiment information which describes the execution of an experiment using a well-defined semantics, and aggregates various aspects of workflow execution including provenance, performance, resource information, and others. Such multi-aspect semantic-rich information is indispensable to build knowledge services on top of it. We describe a grid workflow monitoring architecture which is necessary to collect and correlate workflow monitoring data. The process of aggregation of monitoring data into experiment information is presented. Our approach is validated on a drug resistance ranking application running in the ViroLab virtual laboratory for infectious diseases.