Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL

Autor:	Spahiu, B, Maurino, A, Palmonari, M
Přispěvatelé:	Blerina Spahiu, Andrea Maurino, Matteo Palmonari, Elena Demidova, Amrapali J. Zaveri, Elena Simperl, Spahiu, B, Maurino, A, Palmonari, M
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	INF/01 - INFORMATICA Linked Data Data Quality SHACL Data Summarization
Popis:	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
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=od______1299::54102995b3d11913739b3b95cb0835d6 http://hdl.handle.net/10281/219550 Zobrazit plný text záznamu