Analysis and visualisation of RDF resources in Ondex

2005

2011

Currently, the OBO Foundry plays an important role by setting guidelines to formalise the concepts within the biomedical domain. The ontologies within the OBO Foundry are usually represented in the OBO ontology language. While being humanreadable, this language lacks the computational rigour required for the Semantic Web (SW). Consequently, the RDF and OWL languages, both fundamental components of the SW technology stack, are being increasingly adopted by the biomedical community to exchange biological knowledge in a computer

Database

In the life sciences, researchers increasingly want to access multiple databases in an integrated way. However, different databases currently use different formats and vocabularies, hindering the proper integration of heterogeneous life science data. Adopting the Resource Description Framework (RDF) has the potential to address such issues by improving database interoperability, leading to advances in automatic data processing. Based on this idea, we have advised many Japanese database development groups to expose their databases in RDF. To further promote such activities, we have developed an RDF-based life science dataset repository called the National Bioscience Database Center (NBDC) RDF portal. All the datasets in this repository have been reviewed by the NBDC to ensure interoperability and queryability. As of July 2018, the service includes 21 RDF datasets, comprising over 45.5 billion triples. It provides SPARQL endpoints for all datasets, useful metadata and the ability to download RDF files. The NBDC RDF portal can be accessed at https://integbio.jp/rdf/.

BMC bioinformatics, 2008

Background: The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base. Results: RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented. Conclusions: The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.

Semantic web, 2013

BioPortal is a repository of biomedical ontologies-the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF version of all these ontologies at http://sparql.bioontology.org. This dataset contains 190M triples, representing both metadata and content for the 300 ontologies. We use the metadata that the ontology authors provide and simple RDFS reasoning in order to provide dataset users with uniform access to key properties of the ontologies, such as lexical properties for the class names and provenance data. The dataset also contains 9.8M cross-ontology mappings of different types, generated both manually and automatically, which come with their own metadata.

2009

Journal of Biomedical Semantics, 2013

Background: A key activity for life scientists in this post "-omics" age involves searching for and integrating biological data from a multitude of independent databases. However, our ability to find relevant data is hampered by non-standard web and database interfaces backed by an enormous variety of data formats. This heterogeneity presents an overwhelming barrier to the discovery and reuse of resources which have been developed at great public expense.To address this issue, the open-source Bio2RDF project promotes a simple convention to integrate diverse biological data using Semantic Web technologies. However, querying Bio2RDF remains difficult due to the lack of uniformity in the representation of Bio2RDF datasets. Results: We describe an update to Bio2RDF that includes tighter integration across 19 new and updated RDF datasets. All available open-source scripts were first consolidated to a single GitHub repository and then redeveloped using a common API that generates normalized IRIs using a centralized dataset registry. We then mapped dataset specific types and relations to the Semanticscience Integrated Ontology (SIO) and demonstrate simplified federated queries across multiple Bio2RDF endpoints. Conclusions: This coordinated release marks an important milestone for the Bio2RDF open source linked data framework. Principally, it improves the quality of linked data in the Bio2RDF network and makes it easier to access or recreate the linked data locally. We hope to continue improving the Bio2RDF network of linked data by identifying priority databases and increasing the vocabulary coverage to additional dataset vocabularies beyond SIO.

Pacific Symposium on …, 2006

As the number, richness and diversity of biological sources grow, scientists are increasingly confronted with the problem of selecting appropriate sources and tools. To address this problem, we have designed BioGuide 1 , a user-centric framework that helps scientists choose sources and tools according to their preferences and strategy, by specifying queries through a user-friendly visual interface. In this paper, we provide a complete RDF representation of BioGuide and introduce XPR (eXtensible Path language for RDF), an extension of FSL 2 that is expressive enough to model all BioGuide queries. BioGuide queries modeled as XPR expressions can then be saved, compared, evaluated and exchanged through the Web between users and applications.

2005

BMC Bioinformatics, 2009

Background: As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources.