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Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings,使用知识库嵌入改进知识图上的多跳问答

摘要

知识图(KG)是由实体作为节点,实体之间的关系作为类型化边组成的多关系图。 KG问答(KGQA)任务的目的是回答对KG提出的自然语言查询。 多跳KGQA要求在KG的多个边缘进行推理,以得出正确的答案。 KG通常缺少许多链接,这给KGQA尤其是多跳KGQA带来了额外的挑战。 最近对多跳KGQA的研究已尝试使用相关的外部文本来处理KG稀疏性,但这种方式并非一帆风顺。 在另一项研究中,提出了KG嵌入方法,以通过执行丢失的链接预测来减少KG稀疏性。 此类KG嵌入方法尽管非常相关,但迄今为止尚未针对多跳KGQA进行探索。 我们在本文中填补了这一空白,并提出了EmbedKGQA。 EmbedKGQA在执行稀疏KG上的多跳KGQA方面特别有效(但是当知识图谱不稀疏时,也应该能够超过基线)。 EmbedKGQA还放宽了从预先指定的邻域中选择答案的要求,这是先前的多跳KGQA方法实施的次优约束。 通过在多个基准数据集上进行的广泛实验,我们证明了EmbedKGQA在其他最新基准上的有效性。

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翻译-2020使用知识库嵌入改进知识图上的多跳问答.pdf
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