Exposing Provenance Metadata Using Different RDF Models

A standard model for exposing structured provenance metadata of scientific assertions on the Semantic Web would increase interoperability, discoverability, reliability, as well as reproducibility for scientific discourse and evidence-based knowledge discovery. Several Resource Description Framework (RDF) models have been proposed to track provenance. However, provenance metadata may not only be verbose, but also significantly redundant. Therefore, an appropriate RDF provenance model should be efficient for publishing, querying, and reasoning over Linked Data. In the present work, we have collected millions of pairwise relations between chemicals, genes, and diseases from multiple data sources, and demonstrated the extent of redundancy of provenance information in the life science domain. We also evaluated the suitability of several RDF provenance models for this crowdsourced data set, including the N-ary model, the Singleton Property model, and the Nanopublication model. We examined query performance against three commonly used large RDF stores, including Virtuoso, Stardog, and Blazegraph. Our experiments demonstrate that query performance depends on both RDF store as well as the RDF provenance model

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

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

Towards information profiling: data lake content metadata management

There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft

BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process