Knowledge graphs (KGs) are increasingly used for solving or supporting tasks such as question answering or recommendation. To achieve a useful performance on such tasks, it is important that the knowledge modeled by KGs is as correct and complete as possible. While this is an elusive goal for many domains, techniques for automated KG construction (AKGC) serve as a means to approach it. Yet, AKGC has many open challenges, like learning expressive ontologies or incorporating long-tail entities. With CaLiGraph, we present a KG automatically constructed from categories and lists in Wikipedia, offering a rich taxonomy with semantic class descriptions and a broad coverage of entities. We describe its extraction framework and provide details about its purpose, resources, usage, and quality. Further, we evaluate the performance of CaLiGraph on downstream tasks and compare it to other popular KGs.
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