Background The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools. Objective The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems. Methods We conducted a systematic review of early COVID-19–related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas. Results The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)–powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement. Conclusions In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.

A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED's graph are domain and task independent making the tool suitable to be used as a semantic middleware for domain-or task-specific applications. To serve this purpose, it is available both as REST service and as Python library. This paper provides details about FRED's capabilities, design issues, implementation and evaluation.

We present Tìpalo, an algorithm and tool for automatically typing DBpedia entities. Tìpalo identifies the most appropriate types for an entity by interpreting its natural language definition, which is extracted from its corresponding Wikipedia page abstract. Types are identified by means of a set of heuristics based on graph patterns, disambiguated to WordNet, and aligned to two top-level ontologies: Word-Net supersenses and a subset of DOLCE+DnS Ultra Lite classes. The algorithm has been tuned against a golden standard that has been built online by a group of selected users, and further evaluated in a user study.

Alternative metrics (aka altmetrics) are gaining increasing interest in the scientometrics community as they can capture both the volume and quality of attention that a research work receives online. Nevertheless, there is limited knowledge about their effectiveness as a mean for measuring the impact of research if compared to traditional citation-based indicators. This work aims at rigorously investigating if any correlation exists among indicators, either traditional (i.e. citation count and hindex) or alternative (i.e. altmetrics) and which of them may be effective for evaluating scholars. The study is based on the analysis of real data coming from the National Scientific Qualification procedure held in Italy by committees of peers on behalf of the Italian Ministry of Education, Universities and Research.

No abstract

ArCo is the Italian Cultural Heritage knowledge graph, consisting of a network of seven vocabularies and 169 million triples about 820 thousand cultural entities. It is distributed jointly with a SPARQL endpoint, a software for converting catalogue records to RDF, and a rich suite of documentation material (testing, evaluation, how-to, examples, etc.). ArCo is based on the official General Catalogue of the Italian Ministry of Cultural Heritage and Activities (MiBAC) -and its associated encoding regulations -which collects and validates the catalogue records of (ideally) all Italian Cultural Heritage properties (excluding libraries and archives), contributed by CH administrators from all over Italy. We present its structure, design methods and tools, its growing community, and delineate its importance, quality, and impact.

Abstract. Wikipedia pagelinks, i.e. links between Wikipages, carry an intended semantics: they indicate the existence of a factual relation between the DBpedia entity referenced to by the source Wikipage, and the DBpedia entity referenced to by the target Wikipage of the link. These relations are represented in DBpedia as triple occurrences of a generic "wikiPageWikilinks" property. We designed and implemented a novel method for uncovering the intended semantics of pagelinks, and represent them as semantic relations. In this paper, we experiment our method on a subset of Wikipedia showing its potential impact on DBpedia enrichment.

Abstract.What is the most intuitive way of organizing concepts for describing things? What are the most relevant types of things that people use for describing other things? Wikipedia and Linked Data offer knowledge engineering researchers a chance to empirically identifying invariances in conceptual organization of knowledge i.e. knowledge patterns. In this paper, we present a resource of Encyclopedic Knowledge Patterns that have been discovered by analyizing the Wikipedia page links dataset, describe their evaluation with a user study, and discuss why it enables a number of research directions contributing to the realization of a meaningful Semantic Web.