In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey [2], we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our indepth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace the decision-support framework introduced in [2]. For an informed decision on which KG is best for you we still refer to our in-depth survey.

CrunchBase is a database about startups and technology companies. The data can be searched, browsed, and edited via a website, but is also accessible via an entity-centric HTTP API in JSON format. We present a wrapper around the API that provides the data as Linked Data. The wrapper provides schema-level links to schema.org, Friend-of-a-Friend and Vocabularyof-a-Friend, and entity-level links to DBpedia for organization entities. Further, we describe how to harvest the RDF data to obtain a local copy of the data for further processing and querying that goes beyond the query facilities of the CrunchBase API. Our Linked Data API for CrunchBase and a previous version of it have already been used in two cases, whereas our crawled CrunchBase RDF data set has been used once for data integration and once for information extraction on text. CrunchBase has also been used twice for exploratory data analysis.

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