An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. The proposed model produces a summary that is correct and complete with respect to the assertions of the data set and whose size scales well with respect to the ontology and data size. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.
Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for each and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark.
Open government is emerging as a core issue for increasing, on the one hand, participation of citizens, and, on the other hand, accountability, transparency, and the capability of delivering digital services by Public Administrations, with a consequent interest into public and social value as final outcomes. However, most of the open government initiatives actually concern the provision of public data under an open license and in an open and accessible format. From a policy as well as from a research point of view, open government raises a set of questions about how to manage and evaluate their quality, also considering the compliance with enforced legal frameworks, if any available focused specifically on open government issues. Thus, the paper discusses a quality-based framework for open government data compliance assessment, made up of quality dimensions and a set of criteria contributing to the measurement of a compliance index. The framework is then applied for the evaluation of the open government data initiatives of a sample of local public administration in Italy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.