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First popularized by Google in 2012 (Singhal 2012), knowledge graphs (KG) have now become a staple data representation method. KGs can be described as directed graphs, where the nodes (subject and object entities) represent any type of human knowledge. The edge specifies the relationship between subject and object. KGs are increasingly moving into the (digital) humanities. At their core, the humanities facilitate the preservation, dissemination, and analysis of cultural heritage knowledge. In all three aspects, this work is gradually changing towards the digital, such as digital thesauri. The network structure of KGs in particular opens up new possibilities for data aggregation and data analysis in the humanities. For one, KGs are an opportunity to interlink different fields of study through ontologies. They can also be used as additional sources for text preparation, as a new technology to query, aggregate, and analyze data in new ways, or to discover previously unseen relationships (Hawkins 2022, Zhang et al. 2021). In other words, as per Hyvönen (2020), KGs are not only good for data exploration and solving pre-set problems, they can also be employed for “finding research problems in the first place, for addressing them, and even for solving them automatically under the constraints set by the human researcher.” This abstract introduces the first openly published version of the EncycNet KG, a semantic knowledge graph built from historical German encyclopedias, as well as its potential for the digital humanities. In particular, using EncycNet and Wikidata, we analyze how the representation of countries in encyclopedias has changed from the 19th century until today.
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