Direct relation detection for knowledge-based question answering

Shamsabadi, Abbas Shahini; Ramezani, Reza; Farsani, Hadi Khosravi; Nematbakhsh, Mohammad Ali

doi:10.1016/j.eswa.2022.118678

Cited by 6 publications

(1 citation statement)

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“…They completed the knowledge Q&A in the tourism field using the BERT model. Shamsabadi A. S. et al [27] proposed the use of multiple classifiers in the pipeline in the solution for the identification of hidden relationships, where each classifier uses a separate BiGRU neural network. The questionanswering method based on semantic analysis can independently learn and generate models with complex parameters and functions.…”

Section: Mainstream Technologymentioning

confidence: 99%

Knowledge Base Question Answering via Semantic Analysis

Liu,

Zhang,

Zong

et al. 2023

Electronics

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Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model.

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Section: Mainstream Technologymentioning

confidence: 99%