While a number of quality metrics have been successfully proposed for datasets in the Web of Data, there is a lack of trust metrics that can be computed for any given dataset. We argue that reuse of data can be seen as an act of trust. In the Semantic Web environment, datasets regularly include terms from other sources, and each of these connections express a degree of trust on that source. However, determining what is a dataset in this context is not straightforward. We study the concepts of dataset and dataset link, to finally use the concept of Pay-Level Domain to differentiate datasets, and consider usage of external terms as connections among them. Using these connections we compute the PageRank value for each dataset, and examine the influence of ignoring predicates for computation. This process has been performed for more than 300 datasets, extracted from the LOD Laundromat. The results show that reuse of a dataset is not correlated with its size, and provide some insight on the limitations of the approach and ways to improve its efficacy.
While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users. Question Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user's natural language question from an RDF dataset.In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets.In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses nonexpert users in QA domain.In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets.To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an ondemand, scalable, multilingual, QA system on top of any RDF dataset.
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