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Our daily life is strongly influenced through decision-making processes based on large amounts of data, of which both the
data values as the meaningful (semantic) relationships can be included in knowledge graphs.
Given their automatic processing, knowledge graphs must be of high quality on both these fronts.
This thesis focuses on both improving data quality, as assessing semantic quality of knowledge graphs.
On the one hand, it describes a framework to generate knowledge graphs with extensible data transformations that can clean data ("RML + FnO"), expanded to perform data transformations automatically and implementation-independent ("FnO.io").
On the other hand, it describes a validation approach building on a rule-based reasoning solution ("Validatrr"). This takes into account the semantics used, and enables specific improvements to knowledge graph due to detail root cause explanation of quality problems.
Thanks to these contributions, data values in knowledge graphs are cleaned up while generating knowledge graphs, and they can be completed using automatic data transformations on existing knowledge graphs. Our validation approach makes it possible to accurately assess the quality of semantic relationships in knowledge graphs.
The combined work makes it easier to improve data quality and assess semantic quality for knowledge graphs, which ensures that knowledge graphs can be used correctly in decision-making processes.