As Linked Data available on the Web continue to grow, understanding their structure and assessing their quality remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online semantic profiling tool which helps data consumers in better understanding of the data by extracting data-driven ontology patterns and statistics about the data. The SHACL Shapes Constraint Language helps users capturing quality issues in the data by means of constraints. In this paper we propose a methodology to improve the quality of different versions of the data by means of SHACL constraints learned from the semantic profiles produced by ABSTAT
Spahiu, B., Maurino, A., Palmonari, M. (2018). Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL. In Emerging Topics in Semantic Technologies (pp.103-117). IOS Press [10.3233/978-1-61499-894-5-103].
Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL
Spahiu, B;Maurino, A;Palmonari, M
2018
Abstract
As Linked Data available on the Web continue to grow, understanding their structure and assessing their quality remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online semantic profiling tool which helps data consumers in better understanding of the data by extracting data-driven ontology patterns and statistics about the data. The SHACL Shapes Constraint Language helps users capturing quality issues in the data by means of constraints. In this paper we propose a methodology to improve the quality of different versions of the data by means of SHACL constraints learned from the semantic profiles produced by ABSTAT
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