  Large public knowledge graphs, like Wikidata, contain billions of statements
about tens of millions of entities, thus inspiring various use cases to exploit
such knowledge graphs. However, practice shows that much of the relevant
information that fits users' needs is still missing in Wikidata, while current
linked open data (LOD) tools are not suitable to enrich large graphs like
Wikidata. In this paper, we investigate the potential of enriching Wikidata
with structured data sources from the LOD cloud. We present a novel workflow
that includes gap detection, source selection, schema alignment, and semantic
validation. We evaluate our enrichment method with two complementary LOD
sources: a noisy source with broad coverage, DBpedia, and a manually curated
source with a narrow focus on the art domain, Getty. Our experiments show that
our workflow can enrich Wikidata with millions of novel statements from
external LOD sources with high quality. Property alignment and data quality are
key challenges, whereas entity alignment and source selection are
well-supported by existing Wikidata mechanisms. We make our code and data
available to support future work.
