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首页> 外文期刊>Journal of web semantics: >No one is perfect: Analysing the performance of question answering components over the DBpedia knowledge graph
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No one is perfect: Analysing the performance of question answering components over the DBpedia knowledge graph

机译：没有人是完美的：通过DBPedia知识图表分析问题的绩效回答组件

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摘要

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of Question Answering for user interaction. Existing QA systems have been extensively evaluated as black boxes and their performance has been characterised in terms of average results over all the questions of benchmarking datasets (i.e. macro evaluation). Albeit informative, macro evaluation studies do not provide evidence about QA components' strengths and concrete weaknesses. Therefore, the objective of this article is to analyse and micro evaluate available QA components in order to comprehend which question characteristics impact on their performance. For this, we measure at question level and with respect to different question features the accuracy of 29 components reused in QA frameworks for the DBpedia knowledge graph using state-of-the-art benchmarks. As a result, we provide a perspective on collective failure cases, study the similarities and synergies among QA components for different component types and suggest their characteristics preventing them from effectively solving the corresponding QA tasks. Finally, based on these extensive results, we present conclusive insights for future challenges and research directions in the field of Question Answering over knowledge graphs. (C) 2020 Published by Elsevier B.V.
机译：由于越来越多的知识图表和用户互动应答的问题的不断上升，所以关于过去五年的知识图表的问题回答（QA）已经取得了显着的势头。现有的QA系统已被广泛评估为黑匣子，并且它们的性能已经表现为平均结果，这些基准数据集的所有问题（即宏观评估）。虽然信息丰富的宏观评估研究没有提供关于QA组件的优势和具体弱点的证据。因此，本文的目的是分析和微观评估可用的QA组件，以理解哪个问题对其性能影响。为此，我们在问题级别和不同问题的衡量标题具有使用最先进的基准测试的DBPedia知识图中的QA框架中重复使用的29个组件的准确性。因此，我们提供了对集体故障情况的看法，研究了不同组件类型的QA组件之间的相似性和协同作用，并提出了它们的特性，防止它们有效解决相应的QA任务。最后，基于这些广泛的结果，我们对未来的挑战和研究方向展示了关于知识图表的问题领域的决定性见解。 （c）2020由elsevier b.v发布。

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