论文标题
查询图的方法重新研究知识基础问题回答
A Method of Query Graph Reranking for Knowledge Base Question Answering
论文作者
论文摘要
本文提出了一种新颖的重读方法，可以更好地选择“知识图”的最佳查询图，以检索知识基础问题回答（KBQA）中输入问题的答案。现有方法遇到了严重的问题，即TOP-1性能与TOP-N结果的甲骨文分数之间存在显着差距。为了解决此问题，我们的方法将选择过程分为两个步骤：查询图排名和查询图reranking。在第一步中，我们为每个问题提供顶级查询图。然后，我们建议通过与答案类型的信息结合来重新读取顶级查询图。两个广泛使用的数据集的实验结果表明，我们提出的方法在WebQuestions数据集中获得了最佳结果，并且在复杂问题数据集中获得了第二好的结果。
This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA). Existing methods suffer from a severe problem that there is a significant gap between top-1 performance and the oracle score of top-n results. To address this problem, our method divides the choosing procedure into two steps: query graph ranking and query graph reranking. In the first step, we provide top-n query graphs for each question. Then we propose to rerank the top-n query graphs by combining with the information of answer type. Experimental results on two widely used datasets show that our proposed method achieves the best results on the WebQuestions dataset and the second best on the ComplexQuestions dataset.
