Ingegneria informatica

The Linked Data principles provide a decentral approach for publishing structured data in RDF on the Web. A consequence of this architectural choice is a high variance in the quality of the RDF datasets which constitute the Linked Data cloud. In this demo paper, we address a particular aspect of quality, i.e., the discriminability of resources. During our demo, we will present our simple three-step approach and interface, which allows data publishers to detect the resources in their dataset that are indistinguishable with respect to a given set of properties. Our approach is highly scalable as it relies on ROCKER, a novel algorithm for key discovery. Our evaluation on DBpedia suggests that even very commonly-used data sources are still in need to significant improvement to abide by the discriminability criterion.

In the last years an increasing number of structured data was published on the Web as Linked Open Data (LOD). Despite recent advances, consuming and using Linked Open Data within an organization is still a substantial challenge. Many of the LOD datasets are quite large and despite progress in RDF data management their loading and querying within a triple store is extremely time-consuming and resource-demanding. To overcome this consumption obstacle, we propose a process inspired by the classical Extract-Transform-Load (ETL) paradigm. In this article, we focus particularly on the selection and extraction steps of this process. We devise a fragment of SPARQL dubbed SliceSPARQL, which enables the selection of well-defined slices of datasets fulfilling typical information needs. SliceSPARQL supports graph patterns for which each connected subgraph pattern involves a maximum of one variable or IRI in its join conditions. This restriction guarantees the efficient processing of the query against a sequential dataset dump stream. As a result our evaluation shows that dataset slices can be generated an order of magnitude faster than by using the conventional approach of loading the whole dataset into a triple store and retrieving the slice by executing the query against the triple store's SPARQL endpoint.

Over the last decades many machine learning experiments have been published, giving benefit to the scientific progress. In order to compare machine-learning experiment results with each other and collaborate positively, they need to be performed thoroughly on the same computing environment, using the same sample datasets and algorithm configurations. Besides this, practical experience shows that scientists and engineers tend to have large output data in their experiments, which is both difficult to analyze and archive properly without provenance metadata. However, the Linked Data community still misses a lightweight specification for interchanging machine-learning metadata over different architectures to achieve a higher level of interoperability. In this paper, we address this gap by presenting a novel vocabulary dubbed MEX. We show that MEX provides a prompt method to describe experiments with a special focus on data provenance and fulfills the requirements for a long-term maintenance.