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SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
Mattia Atzeni · Jasmina Bogojeska · Andreas Loukas

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @

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.

Author Information

Mattia Atzeni (Swiss Federal Institute of Technology Lausanne)
Jasmina Bogojeska (International Business Machines)
Andreas Loukas (EPFL, MIT)

Researcher fascinated by graphs and machine learning.

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