  In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to
facts in a knowledge base resulting in quadruples between entities such as
(Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds
within a time interval or at a timestamp. We propose a reinforcement learning
agent gathering temporal relevant information about the query entities'
neighborhoods, simultaneously. We refer to the encodings of the explored graph
structures as fingerprints which are used as input to a Q-network. Our agent
decides sequentially which relation type needs to be explored next to expand
the local subgraphs of the query entities. Our evaluation shows that the
proposed method yields competitive results compared to state-of-the-art
embedding algorithms for tKGs, and we additionally gain information about the
relevant structures between subjects and objects.
