On Reasoning with RDF Statements about Statements using Singleton Property Triples

The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties

Decentralized provenance-aware publishing with nanopublications

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

The use of foundational ontologies in biomedical research

Background: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. Results: From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). Conclusion: Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.</p

Infrastructure for synthetic health data

editorial reviewedMachine learning (ML) methods are becoming ever more prevalent across all domains of lifesciences. However, a key component of effective ML is the availability of large datasets thatare diverse and representative. In the context of health systems, with significant heterogeneityof clinical phenotypes and diversity of healthcare systems, there exists a necessity to developand refine unbiased and fair ML models. Synthetic data are increasingly being used to protectthe patient’s right to privacy and overcome the paucity of annotated open-access medical data. Here, we present our proof of concept for the generation of synthetic health data and our proposed FAIR implementation of the generated synthetic datasets. The work was developed during and after the one-week-long BioHackathon Europe, by together 20 participants (10 new to the project), from different countries (NL, ES, LU, UK, GR, FL, DE, . . . ).</p

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Estudi de l'acoblament magnètic en complexos heterometàl·lics amb lligands pont oxamido, oxamato, tiooxalato i anàlegs

Aquest treball teòric estudia l'acoblament magnètic en complexos bi- i trinuclears heterometàl·lics amb lligands pont oxamido, oxamato, ditiooxalato i anàlegs. Per calcular la seva estructura electrònica s'han usat mètodes multireferencials, en particular diferents variants del mètode DDCI, desenvolupat en el grup, i el mètode CASPT2. Per diferents sistemes binuclears coneguts de Cu(II) i Mn(II), l'acoblament magnètic i els mapes de densitat de spin calculats reprodueixen acuradament les dades experimentals. L'acoblament antiferromagnètic en aquests depèn de la transferència de càrrega del lligand al metall, lligada a l'electronegativitat dels àtoms coordinats. En els sistemes hipotètics de tipus Cu(II)-M(II)-Cu(II), on M=Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu i Zn, la magnitud de l'acoblament estimada depèn de l'electronegativitat del metall central, anant de feblement ferromagnètic pel Sc a moderadament antiferromagnètic pel Cu. Aquest treball aporta la interpretació microscòpica de l'acoblament en aquests sistemes, així com la validació i/o les limitacions dels mètodes de càlcul emprats.This theoretical work examines the magnetic coupling in bi- and trinuclear heterometallic transition metal complexes with bridging ligands such as oxamido, oxamato, ditiooxalato and analogues. To calculate their electronic structure multireference methods have been used, including different variants of DDCI method, developed in our group, and CASPT2 method. For different Cu(II)-Mn(II) binuclear known systems, the magnetic couplings and spin density maps calculated accurately reproduce the experimental data. The antiferromagnetic coupling in these compounds depends on the charge transfer from ligand to metal, linked to the electronegativity of coordinated atoms. In the hypothetical systems of type Cu(II)-M(II)-Cu(II), where M = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu and Zn, the magnitude of the estimated coupling depends on the electronegativity of the metal core, going from the weakly ferromagnetic for Sc to moderately antiferromagnetic for Cu. This work provides the microscopic interpretation of the coupling in these systems, as well as the validation and/or limitations of the computational methods used

Updating The SynthDNASim tool to create diverse synthetic DNA datasets

In biomedical research, it is common to perform numerous analyses of genomic data, for example, to understand the cause of a particular disease. Regulatory laws protect the privacy of individuals, but hinder access to genomic data. One solution to this is the development of bioinformatics tools to create synthetic DNA data. One of the challenges is to capture genomic diversity representative of differences within and between populations, especially for rare genetic diseases. In this study, we present SynthDNASim, a tool for creating diverse synthetic DNA datasets. Our approach is to create diverse DNA datasets considering factors of genetic evolution and ancestry with Huntington’s disease (HD) as a use case. In particular, with HD variants from European, African, and Middle Eastern populations. We will show our tool and plan on applying semantic methods and tools to make SynthDNASim more FAIR