Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.

With the advent of RDFa and the at least partial support by major search engines, semantically structured data is more and more appearing on the Web. To enable high value use cases, links between entity descriptions need to be established. The linked data model suggests that links should be state explicitly by those who expose entity descriptions, but unlike on the normal web, incentives for doing so are unclear so that the model ultimately seems to fail in practice. In this position paper, we make the conjecture that explicit links are not needed for realizing the semantic web. We propose discuss how Record Linkage techniques are in general very well suited for the task but argue the need for a tool would allow data publishers to have an active role in producing entity descriptions that can then be linked automatically.