  Many digital libraries recommend literature to their users considering the
similarity between a query document and their repository. However, they often
fail to distinguish what is the relationship that makes two documents alike. In
this paper, we model the problem of finding the relationship between two
documents as a pairwise document classification task. To find the semantic
relation between documents, we apply a series of techniques, such as GloVe,
Paragraph-Vectors, BERT, and XLNet under different configurations (e.g.,
sequence length, vector concatenation scheme), including a Siamese architecture
for the Transformer-based systems. We perform our experiments on a newly
proposed dataset of 32,168 Wikipedia article pairs and Wikidata properties that
define the semantic document relations. Our results show vanilla BERT as the
best performing system with an F1-score of 0.93, which we manually examine to
better understand its applicability to other domains. Our findings suggest that
classifying semantic relations between documents is a solvable task and
motivates the development of recommender systems based on the evaluated
techniques. The discussions in this paper serve as first steps in the
exploration of documents through SPARQL-like queries such that one could find
documents that are similar in one aspect but dissimilar in another.
