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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pcapks3huberdozbvfqowysuly" style="color: black;">Semantic Web Journal</a>
In the recent years, different web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/sw-160218">doi:10.3233/sw-160218</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uiicnh2nbnev5j4ixum23hzila">fatcat:uiicnh2nbnev5j4ixum23hzila</a> </span>
more »... ry to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.
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