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The goal of Question Answering over Knowledge Graphs (KGQA) is to findanswers for natural language questions over a knowledge graph. Recent KGQAapproaches adopt a neural machine translation (NMT) approach, where the naturallanguage question is translated into a structured query language. However, NMTsuffers from the out-of-vocabulary problem, where terms in a question may nothave been seen during training, impeding their translation. This issue isparticularly problematic for the millions of entities that large knowledgegraphs describe. We rather propose a KGQA approach that delegates theprocessing of entities to entity linking (EL) systems. NMT is then used tocreate a query template with placeholders that are filled by entitiesidentified in an EL phase. Slot filling is used to decide which entity fillswhich placeholder. Experiments for QA over Wikidata show that our approachoutperforms pure NMT: while there remains a strong dependence on having seensimilar query templates during training, errors relating to entities aregreatly reduced.

Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking