Question answering over knowledge graphs (QA-
KGs) is a vital topic within information retrieval.
Questions with temporal intent are a special case
of questions for question answering (QA) systems
that has not received a large amount of attention in
research. In this paper we propose using temporal
knowledge graph embeddings (TKGEs) for tempo-
ral QA. We propose MATQA, a microservice-based
architecture for building temporal QA systems on
knowledge graph embeddings (KGEs). Further-
more, we present a variation of ensemble learning,
Bayesian model averaging (BMA), where results
of several link prediction tasks on separate differ-
ent pre-trained TKGE models are combined and
re-ranked, before being chosen as the final results.
Our experiments on two datasets, ICEWS14 and
ICEWS05-15, performed using this variation of en-
semble, which we build using the microservice-
based architecture, show that it provides better re-
sults, than using these TKGE models individually.