Case-based Reasoning for Natural Language Queries over Knowledge Bases

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-...

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Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Das, Rajarshi, Manzil Zaheer, Thai, Dung, Godbole, Ameya, Perez, Ethan, Jay-Yoon, Lee, Tan, Lizhen, Polymenakos, Lazaros, McCallum, Andrew
Format: Article
Language:English
Subjects:
Datasets
Knowledge bases (artificial intelligence)
Queries
Questions
Reasoning
Online Access:Get full text
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Summary:It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the ComplexWebQuestions dataset, CBR-KBQA outperforms the current state of the art by 11\% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.
ISSN:2331-8422