Knowledge Graph Simple Question Answering for Unseen Domains
This addresses the challenge of adapting KGSQA systems to new, unseen domains in knowledge graphs, which is an incremental advancement for improving question answering in dynamic knowledge bases.
The paper tackles the problem of knowledge graph simple question answering (KGSQA) for unseen domains added during test time, where training data does not cover new relations, and proposes a data-centric domain adaptation framework with question generation and keyword incorporation, resulting in significant improvements over zero-shot baselines and robustness across domains.
Knowledge graph simple question answering (KGSQA), in its standard form, does not take into account that human-curated question answering training data only cover a small subset of the relations that exist in a Knowledge Graph (KG), or even worse, that new domains covering unseen and rather different to existing domains relations are added to the KG. In this work, we study KGSQA in a previously unstudied setting where new, unseen domains are added during test time. In this setting, question-answer pairs of the new domain do not appear during training, thus making the task more challenging. We propose a data-centric domain adaptation framework that consists of a KGSQA system that is applicable to new domains, and a sequence to sequence question generation method that automatically generates question-answer pairs for the new domain. Since the effectiveness of question generation for KGSQA can be restricted by the limited lexical variety of the generated questions, we use distant supervision to extract a set of keywords that express each relation of the unseen domain and incorporate those in the question generation method. Experimental results demonstrate that our framework significantly improves over zero-shot baselines and is robust across domains.