Article
Version 1
Preserved in Portico This version is not peer-reviewed
Retrieval-augmented Knowledge Graph Reasoning for Commonsense Question Answering
Version 1
: Received: 5 June 2023 / Approved: 6 June 2023 / Online: 6 June 2023 (09:45:47 CEST)
A peer-reviewed article of this Preprint also exists.
Sha, Y.; Feng, Y.; He, M.; Liu, S.; Ji, Y. Retrieval-Augmented Knowledge Graph Reasoning for Commonsense Question Answering. Mathematics 2023, 11, 3269. Sha, Y.; Feng, Y.; He, M.; Liu, S.; Ji, Y. Retrieval-Augmented Knowledge Graph Reasoning for Commonsense Question Answering. Mathematics 2023, 11, 3269.
Abstract
Existing Knowledge Graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieved entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel Retrieval-augmented Knowledge Graph (RAKG) model, which solves the above issues through two key innovations. First, we leverage the density matrix to make the model reason along the corrected knowledge path and extract an enhanced knowledge graph subgraph. Second, we fuse representations of questions and graph entities through a bidirectional attention strategy, in which two representations fuse and update by Graph Convolutional Network (GCN). To evaluate the performance of our method, we conduct experiments on two widely-used benchmark datasets CommonsenseQA and OpenBookQA. The case study gives insight into findings that the augmented subgraph provides reasoning along the corrected knowledge path for question answering.
Keywords
Commonsense question answering; Knowledge Graph; Graph Convolutional Network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment