The EBI RDF platform: linked open data for the life sciences

Jupp, Simon; Malone, James; Bolleman, Jerven; Brandizi, Marco; Davies, Mark; García, Leyla; Gaulton, Anna; Géhant, Sébastien; Laibe, Camille; Redaschi, Nicole; Wimalaratne, Sarala M.; Martin, Maria Jesus; Novère, Nicolas Le; Parkinson, Helen; Birney, Ewan; Jenkinson, Andy

doi:10.1093/bioinformatics/btt765

Cited by 206 publications

(168 citation statements)

References 11 publications

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“…Life Sciences are also a prime application area for novel machine learning methods [2,51]. Similarly, Semantic Web technologies such as knowledge graphs and ontologies are widely applied to represent, interpret and integrate data [12,32,61]. There are many reasons for the success of symbolic representations in the Life Sciences.…”

Section: Data and Knowledge In Research -The Case Of The Life Sciencesmentioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: anonymous reviewer; Rinke Hoekstra (https://orcid.org/0000-0001-7076-9083); Agnieszka Ławrynowicz (https://orcid.org/0000-0002-2442-345X); Honghan Wu (https://orcid.org/0000-0002-0213-5668) Received 21 February 2017 Accepted 27 April 2017Abstract. Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox.

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Section: Data and Knowledge In Research -The Case Of The Life Sciencesmentioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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“…Using RDF makes it possible to distribute our data as linked open data (LOD) on the internet using RDF data model. This makes it easy to link our datasets to pertinent internal and external datasets from the LOD cloud (for example, BioModels, BioPortal, BioGateway, and BioSamples) [68,69]. Linking these and other social media datasets is needed in the near future to understand complex questions arising in anti-microbial resistance and phylodynamic analysis.…”

Section: Why Rdf?mentioning

confidence: 99%

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Hasan

Fox

Bisset

et al. 2017

J Healthc Inform Res

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Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

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“…This Web of Data, recognized as a foundation for the Semantic Web, aims to support sharing, dissemination, and re-analysis of datasets while establishing and promoting best practices in specific technical areas. Data from neXtProt [5], UniProtKB, ChEMBL Reactome, BioModels [6], and ExpressionAtlas [7], are examples of curated knowledge that have been provided by the resources themselves in the Resource Description Framework (RDF) format. Extending these prototypes by adapting individual annotations to the Semantic Web nanopublication format has also been explored [8].…”

Section: Creating a Unified Information Spacementioning

confidence: 99%