AFS Graph: Multidimensional Axiomatic Fuzzy Set Knowledge Graph for Open-Domain Question Answering

Open-domain question answering (QA) tasks require a model to retrieve inference chains associated with the answer from massive documents. The core of a QA model is the information filtering ability and reasoning ability. This article proposes a semantic knowledge reasoning graph model based on the m...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2023-12, Vol.34 (12), p.10904-10918
Main Authors: Lang, Qi, Liu, Xiaodong, Jia, Wenjuan
Format: Article
Language:English
Subjects:
Axiomatic fuzzy set (AFS)
Cognition
Documents
Fuzzy logic
Fuzzy sets
Internet
knowledge reasoning
Knowledge representation
Predictive models
question answering (QA)
Questions
Reasoning
Semantics
Task analysis
Transformers
Unsupervised learning
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Summary:Open-domain question answering (QA) tasks require a model to retrieve inference chains associated with the answer from massive documents. The core of a QA model is the information filtering ability and reasoning ability. This article proposes a semantic knowledge reasoning graph model based on the multidimensional axiomatic fuzzy set (AFS), which can generate the knowledge graph (KG) and build reasoning paths for reading comprehension tasks through unsupervised learning. Moreover, taking advantage of the interpretable AFS framework enables the proposed model to have the ability to learn and analyze the semantic relationships between candidate documents. Meanwhile, the utilization of the multidimensional AFS acquires semantic descriptions of candidate documents more concise and flexible. The similarity degree between paragraphs is calculated according to the AFS description to generate the graph. Interpretable chains of reasoning provided by the AFS knowledge graph (AFS Graph) will serve as the basis for the answer prediction. Compared with the previous methods, the AFS Graph model presented in this article improves interpretability and reasoning ability. Experimental results show that the proposed model can achieve the state-of-the-art performance on datasets of HotpotQA, SQuAD, and Natural Questions Open.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3171677