Exchanging Data amongst Linked Data applications

The goal of data exchange is to populate the data model of a target application using data that come from one or more source applications. It is common to address data exchange building on correspondences that are transformed into executable mappings. The problem that we address in this article is how to generate executable mappings in the con-text of Linked Data applications, that is, applications whose data models are semantic-web ontologies. In the literature, there are many proposals to generate executable mappings. Most of them focus on relational or nested-relational data models, which cannot be applied to our context; unfortunately, the few proposals that focus on ontologies have important drawbacks, namely: they solely work on a subset of taxonomies, they require the target data model to be pre-populated or they interpret correspondences in isolation, not to mention the propos-als that actually require the user to handcraft the executable mappings. In this article, we present MostoDE, a new automated proposal to generate SPARQL executable mappings in the context of Linked Data applications. Its salient features are that it does not have any of the previous drawbacks, it is computationally tractable and it has been validated using a series of experiments that prove that it is very efficient and effective in practice

From raw publications to linked data

The continuous development of the Linked Data Web depends on the advancement of the underlying extraction mechanisms. This is of particular interest for the scientific publishing domain, where currently most of the data sets are being created manually. In this article, we present a Machine Learning pipeline that enables the automatic extraction of heading metadata (i. e., title, authors, etc) from scientific publications. The experimental evaluation shows that our solution handles very well any type of publication format and improves the average extraction performance of the state of the art with around 4%, in addition to showing an increased versatility. Finally, we propose a flexible Linked Data-driven mechanism to be used both for refining and linking the automatically extracted metadata