Boosting Knowledge Graph Generation from Tabular Data with RML Views
A large amount of data is available in tabular form. RML is commonly used to declare how such data can be transformed into RDF. However, RML presents limitations that lead, in many cases, to the need for additional preprocessing using scripting. Although some proposed extensions (e.g., FnO or RML fields) address some of these limitations, they are verbose, unfamiliar to most data engineers, and implemented in systems that do not scale up when large volumes of data need to be processed. In this work, we expand RML views to tabular sources so as to address the limitations of this mapping language. In this way, transformation functions, complex joins, or mixed syntax can be defined directly in SQL queries. We present our extension of Morph-KGC to efficiently support RML views for tabular sources. We validate our implementation adapting R2RML test cases with views and compare it against state-of-the-art RML+FnO systems showing that our system is significantly more scalable. Moreover, we present specific examples of a real use case in the public procurement domain where basic RML mappings could not be used without additional preprocessing.
PDF Abstract
Colab
