Starting with Cyc in the 1980s [6], the collection of general knowledge in machine interpretable form has been considered a valuable ingredient in intelligent and knowledge intensive applications. Notable contributions in the eld include the Wikipedia-based datasets DBpedia [5] and YAGO [10], as well as the collaborative knowledge base Wikidata [11]. Since Google has coined the term in 2012, they are most often referred to as knowledge graphs [1, 8]. Besides such open knowledge graphs, many companies have started using corporate knowledge graphs as a means of information representation [7]. In this talk, I will look at two ongoing projects related to the extraction of knowledge graphs from Wikipedia and other Wikis. The rst new dataset, CaLiGraph1, aims at the generation of explicit formal de nitions from categories [2], and the extraction of new instances from list pages [9]. In its current release, CaLiGraph contains 200k axioms de ning classes, and more than 7M typed instances. In the second part, I will look at the transfer of the DBpedia approach to a multitude of arbitrary Wikis. The rst such prototype, DBkWik 2, extracts data from Fandom, a Wiki farm hosting more than 400k different Wikis on various topics. Unlike DBpedia, which relies on a larger user base for crowdsourcing an explicit schema and extraction rules, and the \one-page-per-entity" assumption, DBkWik has to address various challenges in the elds of schema learning and data integration [3, 4]. In its current release, DBkWik contains more than 11M entities, and has been found to be highly complementary to DBpedia.