Tabular datasets are common in many domains, for example science and engineering. These are often not very well specified, and are therefore hard to understand and use. Semantic standards are available to express the meaning and context of the data. However, present standards have their limitations in expressing heterogeneous datasets with several types of measurements, missing data, and irregular structures. Such datasets are abundant in everyday life. We propose the RDF (Resource Description Framework) Record Table vocabulary for semantically modelling tabular data, as a supplement to the existing RDF Data Cube standard. RDF Record Table has a nested structure of records that contain self-describing observations, and is able to cope with irregular, missing and unexpected data. This allows it to escape the constraints of RDF Data Cube and to model complex data, such as that occurring in science and engineering. We demonstrate our Excel add-in for transforming data into the Record Table format. We propose a general approach to integrating tabular data in RDF, and confirm this approach by implementing integration support in the add-in and evaluating this in industrial use cases. This semantic support for tables helps researchers and data analysts to get the most out of available quantitative data with a minimum of effort.
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