Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable, and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. First, to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Second, we argue that to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism via a single entry link. To address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset, (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.

A. Haller, J. Fernández, M. Kamdar, A. Polleres, What are links in linked open data? A characterization and evaluation of links between knowledge graphs on the web, Journal of Data and Information Quality 12 (2) (2020)