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Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents
Version 1
: Received: 13 October 2021 / Approved: 14 October 2021 / Online: 14 October 2021 (15:40:48 CEST)
A peer-reviewed article of this Preprint also exists.
Liu, S.; Xu, R.; Duan, L.; Li, M.; Liu, Y. Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents. Sensors 2021, 21, 8439. Liu, S.; Xu, R.; Duan, L.; Li, M.; Liu, Y. Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents. Sensors 2021, 21, 8439.
Abstract
The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.
Keywords
metaknowledge; graph modeling; question answering; graph neural networks; knowledge graph
Subject
Computer Science and Mathematics, Computer Science
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.
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