Benchmarking question answering systems.

The necessity of making the Semantic Web more accessible for lay users, alongside the uptake of interactive systems and smart assistants for the Web, have spawned a new generation of RDF-based question answering systems. However, fair evaluation of these systems remains a challenge due to the different type of answers that they provide. Hence, repeating current published experiments or even benchmarking on the same datasets remains a complex and time-consuming task. We present a novel online benchmarking platform for question answering (QA) that relies on the FAIR principles to support the fine-grained evaluation of question answering systems. We detail how the platform addresses the fair benchmarking platform of question answering systems through the rewriting of URIs and URLs. In addition, we implement different evaluation metrics, measures, datasets and pre-implemented systems as well as methods to work with novel formats for interactive and non-interactive benchmarking of question answering systems. Our analysis of current frameworks shows that most of the current frameworks are tailored towards particular datasets and challenges but do not provide generic models. In addition, while most frameworks perform well in the annotation of entities and properties, the generation of SPARQL queries from annotated text remains a challenge.