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A Consolidated Dataset for Knowledge-based Question Generation using Predicate Mapping of Linked Data

机译:使用链接数据谓词映射的基于知识的问题生成的合并数据集

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In this paper, we present the ForwardQuestions data set. made of human-generated questions related to knowledge triples. This data set results from the conversion and merger of the existing SimpleDBPediaQA and SimpleQuestionsWikidata data sets, including the mapping of predicates from DBPedia to Wikidata, and the selection of 'forward' questions as opposed to 'backward' ones. The new data set can be used to generate novel questions given an unseen Wikidata triple, by replacing the subjects of existing questions with the new one and then selecting the best candidate questions using semantic and syntactic criteria. Evaluation results indicate that the question generation method using ForwardQuestions improves the quality of questions by about 20% with respect to a baseline not using ranking criteria.
机译:在本文中,我们介绍了ForwardQuestions数据集。由与知识有关的人为问题构成的三倍。该数据集来自现有SimpleDBPediaQA和SimpleQuestionsWiki数据集的转换和合并,包括谓词从DBPedia到Wikidata的映射,以及“正向”问题与“向后”问题的选择。通过使用新的数据集替换现有问题的主题,然后使用语义和句法标准选择最佳候选问题,新的数据集可用于在给定看不见的Wikidata三元组的情况下生成新问题。评估结果表明,与不使用排名标准的基准相比,使用ForwardQuestions的问题生成方法将问题的质量提高了约20%。

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