Invited Talk at the 3rd Workshop on Linked Data in Linguistics: Multilingual Knowledge Resources and Natural Language Processing. Reykjavik, Iceland, 27th May 2014 The ideas behind the Web of Linked Data have great allure. Apart from the prospect of large amounts of freely available data, we are also promised nearly effortless interoperability. Common data formats and protocols have indeed made it easier than ever to obtain and work with information from different sources simultaneously, opening up new opportunities in linguistics, library science, and many other areas. In this talk, however, I argue that the true potential of Linked Data can only be appreciated when extensive cross-linkage and integration engenders an even higher degree of interconnectedness. This can take the form of shared identifiers, e.g. those based on Wikipedia and WordNet, which can be used to describe numerous forms of linguistic and commonsense knowledge. An alternative is to rely on sameAs and similarity links, which can automatically be discovered using scalable approaches like the LINDA algorithm but need to be interpreted with great care, as we have observed in experimental studies. A closer level of linkage is achieved when resources are also connected at the taxonomic level, as exemplified by the MENTA approach to taxonomic data integration. Such integration means that one can buy into ecosystems already carrying a range of valuable pre-existing assets. Even more tightly integrated resources like Lexvo.org combine triples from multiple sources into unified, coherent knowledge bases. Finally, I also comment on how to address some remaining challenges that are still impeding a more widespread adoption of Linked Data on the Web. In the long run, I believe that such steps will lead us to significantly more tightly integrated Linked Data.