Ontology-guided Reference Data Alignment in Information Integration Projects

One of the hard problems in information integration projects (harmonizing data from various legacy sources into one or more targets) is the appropriate alignment of reference data values across systems. Without this alignment, the process of loading records into the target systems might fail because the target might reject any record with an unknown reference data value or different underlying data semantics. Today, detecting reference data tables and determining the relative alignment between a source and a target is largely manual, cumbersome, error-prone and costly. We propose a novel ontology-guided approach to detect reference data tables and their relative alignment across source/target systems to enable semi-automated creation of translation tables

Automatic annotation of bioinformatics workflows with biomedical ontologies

Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 myExperiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014 conference), 15 pages, 4 figure

SCIF-IRIS Framework: A framework to facilitate interoperability in supply chains

One approach that allows improving the collaboration among all the enterprises within a supply chain is interoperability. Interoperability allows the enterprises in the supply chain to collaborate in an efficient manner while preserving their own identities and their own ways of doing business through mechanisms that act as facilitators. However, there are few real practical examples of supply chain interoperability that can be used as a reference. In this paper, we present a framework that can facilitate supply chain interoperability and an example of how it can be applied to a food supply chain

Interoperability and FAIRness through a novel combination of Web technologies

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