The amount of Linked Open Data available on the Web is rapidly growing. The quality of the provided data, however, is generally-speaking not fundamentally improving, hampering its wide-scale deployment for many real-world applications. A key data quality aspect for Linked Open Data can be expressed in terms of its adherence to an underlying welldened schema or ontology, which serves both as a documentation for the end-users as well as a xed reference for automated processing over the data. In this paper, we rst report on an analysis of the schema adherence of domains and ranges for Linked Open Data. We then propose new techniques to improve the correctness of domains and ranges by i) identifying the cases in which a property is used in the data with several dierent semantics, and ii) resolving them by updating the underlying schema and/or by modifying the data without compromising its retro-compatibility. We experimentally show the validity of our methods through an empirical evaluation over DBpedia by creating expert judgements of the proposed xes over a sample of the data.
['Alberto Tonon', 'Michele Catasta', 'Gianluca Demartini', 'Philippe Cudré-Mauroux']
Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation.