TERQA: Question answering over knowledge graph considering precise dependencies of temporal information on vectors

Yao, Junping; Wang, Yijing; Li, Xiaojun; Yang, Cong; Cheng, Kai‐Wen

doi:10.1016/j.displa.2022.102269

Cited by 4 publications

(1 citation statement)

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“…TERQA ( Yao et al, 2022 ): On the basis of EXAQT, inspired by capsule network, TERQA improved the fusion of time features and triplet features and learned the exact dependence between time features and triplet facts, which enhanced the accuracy of the model to predict the answer.…”

Section: Methodsmentioning

confidence: 99%

Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework

Yao,

Yuan,

et al. 2023

PeerJ Computer Science

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Answer sorting and filtering are two closely related steps for determining the answer to a question. Answer sorting is designed to produce an ordered list of scores based on Top-k and contextual criteria. Answer filtering optimizes the selection according to other criteria, such as the range of time constraints the user expects. However, the unclear number of answers and time constraints, as well as the high score of false positive results, indicate that the traditional sorting and selection methods cannot guarantee the quality of answers to multi-answer questions. Therefore, this study proposes MATQA, a component based on multi-answer temporal question reasoning, using a re-validation framework to convert the Top-k answer list output by the QA system into a clear number of answer combinations, and a new multi-answer based evaluation index is proposed for this output form. First, the highly correlated subgraph is selected by calculating the scores of the boot node and the related fact node. Second, the subgraph attention inference module is introduced to determine the initial answer with the highest probability. Finally, the alternative answers are clustered at the semantic level and the time constraint level. Meanwhile, the candidate answers with similar types and high scores but do not satisfy the semantic constraints or the time constraints are eliminated to ensure the number and accuracy of final answers. Experiments on the multi-answer TimeQuestions dataset demonstrate the effectiveness of the answer combinations output by MATQA.

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Section: Methodsmentioning

confidence: 99%