  In the current paper, we present the KG-FRUS dataset, comprised of more than
300,000 US government diplomatic documents encoded in a Knowledge Graph (KG).
We leverage the data of the Foreign Relations of the United States (FRUS)
(available as XML files) to extract information about the documents and the
individuals and countries mentioned within them. We use the extracted entities,
and associated metadata, to create a graph-based dataset. Further, we
supplement the created KG with additional entities and relations from Wikidata.
The relations in the KG capture the synergies and dynamics required to study
and understand the complex fields of diplomacy, foreign relations, and
politics. This goes well beyond a simple collection of documents which neglects
the relations between entities in the text. We showcase a range of
possibilities of the current dataset by illustrating different approaches to
probe the KG. In the paper, we exemplify how to use a query language to answer
simple research questions and how to use graph algorithms such as Node2Vec and
PageRank, that benefit from the complete graph structure. More importantly, the
chosen structure provides total flexibility for continuously expanding and
enriching the graph. Our solution is general, so the proposed pipeline for
building the KG can encode other original corpora of time-dependent and complex
phenomena. Overall, we present a mechanism to create KG databases providing a
more versatile representation of time-dependent related text data and a
particular application to the all-important FRUS database.

Computation and Language