NITELIGHT: A Graphical Tool for Semantic Query Construction

Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries

Semantic Description, Publication and Discovery of Workflows in myGrid

The bioinformatics scientific process relies on in silico experiments, which are experiments executed in full in a computational environment. Scientists wish to encode the designs of these experiments as workflows because they provide minimal, declarative descriptions of the designs, overcoming many barriers to the sharing and re-use of these designs between scientists and enable the use of the most appropriate services available at any one time. We anticipate that the number of workflows will increase quickly as more scientists begin to make use of existing workflow construction tools to express their experiment designs. Discovery then becomes an increasingly hard problem, as it becomes more difficult for a scientist to identify the workflows relevant to their particular research goals amongst all those on offer. While many approaches exist for the publishing and discovery of services, there have been few attempts to address where and how authors of experimental designs should advertise the availability of their work or how relevant workflows can be discovered with minimal effort from the user. As the users designing and adapting experiments will not necessarily have a computer science background, we also have to consider how publishing and discovery can be achieved in such a way that they are not required to have detailed technical knowledge of workflow scripting languages. Furthermore, we believe they should be able to make use of others' expert knowledge (the semantics) of the given scientific domain. In this paper, we define the issues related to the semantic description, publishing and discovery of workflows, and demonstrate how the architecture created by the myGrid project aids scientists in this process. We give a walk-through of how users can construct, publish, annotate, discover and enact workflows via the user interfaces of the myGrid architecture; we then describe novel middleware protocols, making use of the Semantic Web technologies RDF and OWL to support workflow publishing and discovery

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

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

Hypermedia-based discovery for source selection using low-cost linked data interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness