Abstract Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of “groups of duplicates”, which can be used to measure the group’s accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.

Today, several online services offer functionalities to access information from big scholarly communication graphs, which interlink entities such as publications, authors, datasets, organizations, etc. Such graphs are often populated over time as aggregations of multiple sources and therefore suffer from entity duplication problems. Although deduplication of graphs is a known and actual problem, solutions tend to be dedicated and address a few of the underlying challenges. In this paper, we propose the GDup system, an integrated, scalable, general-purpose system for entity deduplication over big information graphs. GDup supports practitioners with the functionalities needed to realize a fully-fledged entity deduplication workflow over a generic input graph, inclusive of Ground Truth support, end-user feedback, and strategies for identifying and merging duplicates to obtain an output disambiguated graph. GDup is today one of the core components of the OpenAIRE infrastructure production system, monitoring Open Science trends on behalf of the European Commission.

The OpenAIRE infrastructure populates a scholarly communication big graph interlinking metadata objects of publications, datasets, software, organizations, funders, and projects. In order to de-duplicate this graph, OpenAIRE has developed GDup, an integrated, scalable, general-purpose system for entity deduplication over big information graphs. GDup offers functionalities to realize a fully-fledged entity deduplication workflow over a generic input graph, inclusive of Ground Truth support, end-user feedback, and strategies for identifying and merging duplicates to obtain an output disambiguated graph.

Open Access and Open Scholarship have revolutionized the way scholarly artefacts are evaluated and published, while the introduction of new technologies and media in scientific workflows has changed the "how" and to "whom" science is communicated, and how stakeholders interact with the scientific community and the broader public. The EU funded project OpenUP is connecting people, information and tools and provides a knowledge hub and a validated framework for the review, assessment and dissemination aspects of the research lifecycle, under the prism of a gender-sensitive Open Science.

Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms such as data deluge and big data today characterize numerous (research) initiatives, much work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, and quality issues to a rather general lack of a data‐sharing culture. Lately, data journals have overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for data set description, availability, citation, quality, and open access. We close by identifying ways to expand and strengthen the data journals approach as a means to promote data set access and exploitation.

With the popularity of social media sites, digital content is increasingly stored and managed online. At the same time, the desktop and local storage continues to provide a personal environment in which users perform their daily tasks. Thus, to accomplish their tasks, users need to continuously switch between local and remote resources and applications, often carrying the burden of coordinating and synchronizing these in a consistent way. In this demonstration, we describe a system, called ScholarLynk, that bridges the local and online worlds and allows users to manage both local and online resources in a uniform way and in collaboration with others.

This paper presents XPeer, a data sharing system for massively distributed XML data. XPeer allows users to publish and query heterogeneous information without any significant administration efforts. XPeer tries to dispatch any given query to all and only the potentially relevant peers, exploiting a superpeer network to this aim.

A part of a query that will never contribute data to the query answer should be regarded as an error. This principle has been recently accepted into mainstream XML query languages, but was still waiting for a complete treatment. We provide here a precise definition for this class of errors, and define a type system that is sound and complete, in its search for such errors, for a core language, under mild restrictions on the use of recursion in type definitions. In the process, we describe a dichotomy among existential and universal type systems, which is essential to understand some specific features of our type system.

If a subexpression in a query will never contribute data to the query answer, this should be regarded as an error. This principle has been recently accepted into mainstream XML query languages, but was still waiting for a complete treatment. We provide here a precise definition for this class of errors, and define a type system that is sound and complete, in its search for such errors, for a core language, under mild restrictions on the use of recursion in type definitions. In the process, we describe a dichotomy among existential and universal type systems, which is useful to understand some unusual features of our type system.

Abstract XML is nowadays considered the standard meta‐language for document markup and data representation. XML is widely employed in Web‐related applications as well as in database applications, and there is also a growing interest for it by the literary community to develop tools for supporting document‐oriented retrieval operations. The purpose of this article is to show the basic new requirements of this kind of applications and to present the main features of a typed query language, called Tequyla‐TX, designed to support them.