The knowledge seeking procedure described in Section 2.1 applies a search algorithm over the graph Each of such queries takes constant time. As mentioned in Section 2.3, the approach described in this paper can be used to answer any valid We proceed by induction on the number of literals |Q |. 3 Base case. For the experiments on KBQA, we assume that we only have access to pairs of questions and answers, i.e. the actual inferential chain leading from the question to the answer is latent. Therefore, we resort to weak supervision to train the model. Inspired by such insight, we employ a similar technique to enhance the performance of our model.

The knowledge seeking procedure described in Section 2.1 applies a search algorithm over the graph Each of such queries takes constant time. As mentioned in Section 2.3, the approach described in this paper can be used to answer any valid We proceed by induction on the number of literals |Q |. 3 Base case. For the experiments on KBQA, we assume that we only have access to pairs of questions and answers, i.e. the actual inferential chain leading from the question to the answer is latent. Therefore, we resort to weak supervision to train the model. Inspired by such insight, we employ a similar technique to enhance the performance of our model.

This produces a set of candidate solutions that can be provably refined to recover the solution to the original problem.

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs. In this paper, we address this issue by showing that multi-hop and more complex logical reasoning can be accomplished separately without losing expressive power. Motivated by this insight, we propose an approach to multi-hop reasoning that scales linearly with the number of relation types in the graph, which is usually significantly smaller than the number of edges or nodes. This produces a set of candidate solutions that can be provably refined to recover the solution to the original problem. Our experiments on knowledge-based question answering show that our approach solves the multi-hop MetaQA dataset, achieves a new state-of-the-art on the more challenging WebQuestionsSP, is orders of magnitude more scalable than competitive approaches, and can achieve compositional generalization out of the training distribution.