Query Path Generation via Bidirectional Reasoning for Multihop Question Answering From Knowledge Bases

Zhang, Geng; Liu, Jin; Zhou, Guangyou; Xie, Zhiwen; Yu, Xia; Cui, Xiaohui

doi:10.1109/tcds.2022.3198272

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Coref-GRU by Dhingra et al [24], MHQA-GRN by Song et al [25], and the breadth first reasoning graph (BFR-Graph) by Huang and Yang [26] exemplify the integration of entity recognition and graph construction to facilitate deeper information analysis. Moreover, the from easy to hard (FE2H) model by Li et al [27] and the bidirectional recurrent graph neural network (BRGNN) by Zhang et al [28] illustrate the ongoing evolution of graph-based reasoning in multi-hop QA, highlighting efforts to minimize errors and leverage relational patterns for improved QA performance.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks

Cai,

Ma,

Hou

et al. 2024

Electronics

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In the rapidly evolving domain of question answering systems, the ability to integrate machine comprehension with relational reasoning stands paramount. This paper introduces a novel architecture, the Dependent Syntactic Semantic Augmented Graph Network (DSSAGN), designed to address the intricate challenges of multi-hop question answering. By ingeniously leveraging the synergy between syntactic structures and semantic relationships within knowledge graphs, DSSAGN offers a breakthrough in interpretability, scalability, and accuracy. Unlike previous models that either fall short in handling complex relational paths or lack transparency in reasoning, our framework excels by embedding a sophisticated mechanism that meticulously models multi-hop relations and dynamically prioritizes the syntactic–semantic context.

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