  Relation linking is essential to enable question answering over knowledge
bases. Although there are various efforts to improve relation linking
performance, the current state-of-the-art methods do not achieve optimal
results, therefore, negatively impacting the overall end-to-end question
answering performance. In this work, we propose a novel approach for relation
linking framing it as a generative problem facilitating the use of pre-trained
sequence-to-sequence models. We extend such sequence-to-sequence models with
the idea of infusing structured data from the target knowledge base, primarily
to enable these models to handle the nuances of the knowledge base. Moreover,
we train the model with the aim to generate a structured output consisting of a
list of argument-relation pairs, enabling a knowledge validation step. We
compared our method against the existing relation linking systems on four
different datasets derived from DBpedia and Wikidata. Our method reports large
improvements over the state-of-the-art while using a much simpler model that
can be easily adapted to different knowledge bases.
