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.

过去五年来，由于大型知识图的可用性不断提高，并且问答对用户交互的重要性日益提高，因此知识图的问答（QA）取得了显着发展。现有的QA系统已被广泛评价为黑匣子，其性能已通过基准数据集所有问题（即宏评估）的平均结果来表征。尽管内容丰富，但宏观评估研究并未提供有关质量保证要素优势和具体劣势的证据。因此，本文的目的是分析和微观评估可用的QA组件，以了解哪些问题特征会影响其性能。为了这，我们使用最新的基准，在问题级别和针对不同的问题特征，对Qped框架在DBpedia知识图的质量保证框架中重复使用的29个组件的准确性进行了测量。结果，我们提供了一个关于集体故障案例的观点，研究了QA组件之间针对不同组件类型的相似性和协同作用，并提出了其特征，从而阻止了他们有效地解决相应的QA任务。最后，基于这些广泛的结果，我们对知识图的问题解答领域中的未来挑战和研究方向提供了结论性见解。研究不同质量类型的质量检查组件之间的相似性和协同作用，并提出其特征，阻止它们有效地解决相应的质量检查任务。最后，基于这些广泛的结果，我们对知识图的问题解答领域中的未来挑战和研究方向提供了结论性见解。研究不同质量类型的质量检查组件之间的相似性和协同作用，并提出其特征，阻止它们有效地解决相应的质量检查任务。最后，基于这些广泛的结果，我们对知识图的问题解答领域中的未来挑战和研究方向提供了结论性见解。