Generative Relation Linking for Question Answering over Knowledge Bases

The goal of Knowledge Base Question Answering (KBQA) systems is to transform natural language questions into SPARQL queries that are then used to retrieve answer(s) from the target Knowledge Base (KB). Relation linking is a crucial component in building KBQA systems. It identifies the relations expressed in the question and maps them to the corresponding KB relations. For example, in Figure 1, to translate the question "What is the owning organization of the Ford Kansas City Assembly Plant and also the builder of the Ford Y-block engine?" into its corresponding SPARQL query, it is necessary to determine the two KB relations: dbo:owningOrganisation, dbo:manufacturer. Relation linking has proven to be a challenging problem, with state-of-the-art approaches performing less than 50% F1 on the majority of the datasets Sakor et al. [2019], Lin et al. [2020], Mihindukulasooriya et al. [2020], thus making it a bottleneck for the overall performance of KBQA systems. The challenges primarily arise from the following factors: 1) relations in text and the KB are often lexicalized differently (implicit mentions); 2) questions with multiple relations and 3) training data is often limited. While past approaches have tried to tackle these issues by either creating hand-coded rules Sakor et al. [2020], or by using semantic parsing Mihindukulasooriya et al. [2020], these challenges can be naturally addressed using the latest advances in auto-regressive sequence-to-sequence models (seq2seq) which have been shown to perform surprisingly well on tasks such as question answering Lewis et al. [2020a], slot filling Petroni et al. [2020] or entity linking Cao et al. [2020], in a generative fashion. However, seq2seq models have not yet been explored for relation linking, particularly in the context of KBQA. In this work, we introduce GenRL, a novel generative approach for relation linking that capitalises on pre-trained seq2seq models.