Detecting Quality Problems in Semantic Metadata without the Presence of a Gold Standard

Detecting quality problems in semantic metadata is crucial for ensuring a high quality semantic web. Current approaches are primarily focused on the algorithms used in semantic metadata generation rather than on the data themselves. They typically require the presence of a gold standard and are not suitable for assessing the quality of semantic metadata. This paper proposes a novel approach, which exploits a range of knowledge sources including both domain and background knowledge to support semantic metadata evaluation without the need of a gold standard. We have conducted a set of preliminary experiments, which show promising results.