Towards Automatic Generation of Semantic Types in Scientific Workflows

Abstract

Abstract. Scientific workflow systems are problem-solving environments that allow scientists to automate and reproduce data management and analysis tasks. Workflow components include actors (e.g., queries, transformations, analyses, simulations, visualizations), and datasets which are produced and consumed by actors. The increasing number of such components creates the problem of discovering suitable components and of composing them to form the desired scientific workflow. In previous work we proposed the use of semantic types (annotations relative to an ontology) to solve these problems. Since creating semantic types can be complex and time-consuming, scalability of the approach becomes an issue. In this paper we propose a framework to automatically derive semantic types from a (possibly small) number of initial types. Our approach propagates the given semantic types through workflow steps whose input and output data structures are related via query expressions. By propagating semantic types, we can significantly reduce the effort required to annotate datasets and components and even derive new "candidate axioms" for inclusion in annotation ontologies.

Readable workflows need simple data

“…Providing quantitative complexity measures together with automated tagging may further increase component and data reuse. Identification of tasks may also support the use of semantic tools in workflow creation 8,11 .…”

“…The difficulty in reusing workflows and research data is not only a waste of time, money and effort but also represents a threat to the basic scientific principle of reproducibility. Current literature on workflows deals with different tools to create and manipulate workflows [5][6][7] as well as to keep track of data provenance 2 and how semantics can be integrated into workflows 8 . However, there is a lack of papers that discuss workflow components within an analysis including data processing.…”

Automatic Description of Context-Altering Services through Observational Learning

“…Bowers et al exploit the additional knowledge contained in scientific workflows for annotation purposes [2]. Scientific workflow systems aim to integrate preprocessing of data, statistical, or data mining processes on the data and postprocessing and visualization of the results.…”

Ontology-Driven Composition and Validation of Scientific Grid Workflows in Kepler: a Case Study of Hyperspectral Image Processing

“…The reason is that semantic data types are normally used in symbolically checking the compatibilities among data of different semantic types, not participating real computations. In this study, motivated by the work in [30], we choose to generate semantic data types based on ontologies and will be detailed in next section. We use the semantic data types derived from the data type ontology to annotate ports of remote sensing image processing actors for workflow validations.…”