{'fraction_non_alphanumeric': 0.020191285866099893, 'fraction_numerical': 0.0, 'mean_word_length': 5.322147651006712, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Linked Data Quality

The wides pread of semantic web technologies such as RDF, SPARQL and OWL enables individuals to build their databases on the web, write vocabularies, and define rules to arrange and explain the relationships between data according to the Linked Data principles. As a consequence, a large amount of structured and interlinked data is being generated daily. A close examination of the quality of this data could be very critical, especially if important researches and professional decisions depend on it. Several linked data quality metrics have been proposed, and they cover numerous dimensions of linked data quality such as completeness, consistency, conciseness and interlinking. In this work, we are interested in linked data quality dimensions, especially the completeness and conciseness of linked datasets. A set of experiments were conducted on a real-world dataset (DBpedia) to evaluate our proposed approaches.