  The goal of Question Answering over Knowledge Graphs (KGQA) is to find
answers for natural language questions over a knowledge graph. Recent KGQA
approaches adopt a neural machine translation (NMT) approach, where the natural
language question is translated into a structured query language. However, NMT
suffers from the out-of-vocabulary problem, where terms in a question may not
have been seen during training, impeding their translation. This issue is
particularly problematic for the millions of entities that large knowledge
graphs describe. We rather propose a KGQA approach that delegates the
processing of entities to entity linking (EL) systems. NMT is then used to
create a query template with placeholders that are filled by entities
identified in an EL phase. Slot filling is used to decide which entity fills
which placeholder. Experiments for QA over Wikidata show that our approach
outperforms pure NMT: while there remains a strong dependence on having seen
similar query templates during training, errors relating to entities are
greatly reduced.
