One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user’s goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

Publication and archival of scientific results is still commonly considered the responsability of classical publishing companies. Classical forms of publishing, however, which center around printed narrative articles, no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. In this article, we propose to design scientific data publishing as a web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used as a low-level data publication layer to serve the Semantic Web in general. Our evaluation of the current network shows that this system is efficient and reliable.

Unstructured metadata fields such as ‘description’ offer tremendous value for users to understand cultural heritage objects. However, this type of narrative information is of little direct use within a machine-readable context due to its unstructured nature. This article explores the possibilities and limitations of named-entity recognition (NER) and term extraction (TE) to mine such unstructured metadata for meaningful concepts. These concepts can be used to leverage otherwise limited searching and browsing operations, but they can also play an important role to foster Digital Humanities research. To catalyze experimentation with NER and TE, the article proposes an evaluation of the performance of three third-party entity extraction services through a comprehensive case study, based on the descriptive fields of the Smithsonian Cooper–Hewitt National Design Museum in New York. To cover both NER and TE, we first offer a quantitative analysis of named entities retrieved by the services in terms of precision and recall compared with a manually annotated gold-standard corpus, and then complement this approach with a more qualitative assessment of relevant terms extracted. Based on the outcomes of this double analysis, the conclusions present the added value of entity extraction services, but also indicate the dangers of uncritically using NER and/or TE, and by extension Linked Data principles, within the Digital Humanities. All metadata and tools used within the article are freely available, making it possible for researchers and practitioners to repeat the methodology. By doing so, the article offers a significant contribution towards understanding the value of entity recognition and disambiguation for the Digital Humanities.