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Linked Open Data (LOD) is the publicly available RDF data in the Web. EachLOD entity is identfied by a URI and accessible via HTTP. LOD encodesglobalscale knowledge potentially available to any human as well as artificialintelligence that may want to benefit from it as background knowledge forsupporting their tasks. LOD has emerged as the backbone of applications indiverse fields such as Natural Language Processing, Information Retrieval,Computer Vision, Speech Recognition, and many more. Nevertheless, regardless ofthe specific tasks that LOD-based tools aim to address, the reuse of suchknowledge may be challenging for diverse reasons, e.g. semantic heterogeneity,provenance, and data quality. As aptly stated by Heath et al. Linked Data mightbe outdated, imprecise, or simply wrong": there arouses a necessity toinvestigate the problem of linked data validity. This work reports acollaborative effort performed by nine teams of students, guided by an equalnumber of senior researchers, attending the International Semantic Web ResearchSchool (ISWS 2018) towards addressing such investigation from differentperspectives coupled with different approaches to tackle the issue.

Linked Open Data Validity -- A Technical Report from ISWS 2018