Bio2RDF Release 2: Improved Coverage, Interoperability and Provenance of Life Science Linked Data

Callahan, Alison; Cruz-Toledo, José; Ansell, Peter; Dumontier, Michel

doi:10.1007/978-3-642-38288-8_14

Cited by 108 publications

(115 citation statements)

References 17 publications

Supporting

Mentioning

114

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Linked Data have gained significant uptake in the life sciences as a technology to connect the various data sets that are used by researchers in this field. In particular, the Bio2RDF project (Belleau, Nolin, Tourigny, Rigault, & Morissette, 2008;Callahan et al, 2013) …”

Section: Linked Data In Life Sciencementioning

confidence: 99%

Interlinking Scientific Data on a Global Scale

Bizer

2013

Data Sci. J.

View full text Add to dashboard Cite

“…Due to the increasing size of literature repositories and the number of different dispersed and isolated databases, there are a lot of efforts devoted to efficiently extracting and providing the most up-to-date data in a way that can be integrated with other statements to facilitate knowledge discovery. Among them, text-mining approaches to unlock relationships between biomedical entities from the literature [1], community-driven publication approaches based on Wiki systems [2][3][4], or publishing existing databases under the standards of the Semantic Web integrated in a meaningful way to the Linked Open Data cloud (LOD) [5] like UniProt [6], DisGeNET [7], or more ambitious projects like the publication of entire subnetworks such as Bio2RDF [8] and Linked Life Data [9].…”

Section: Introductionmentioning

confidence: 99%

Publishing DisGeNET as nanopublications

Queralt-Rosiñach

Kuhn²,

Chichester

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for discovery in Life Sciences. The scientific community cannot process assertions from biomedical publications and integrate them into the current knowledge at the same rate. The automatic extraction of assertions about entities and their relationships by text-mining the scientific literature is an extended approach to structure up-to-date knowledge. For knowledge integration, the publication of assertions in the Semantic Web is gaining adoption, but it opens new challenges regarding the tracking of the provenance, and how to ensure versioned data linking. Nanopublications are a new way of publishing structured data that consists of an assertion along with its provenance. Trusty URIs is a novel approach to make resources in the Web immutable, and to ensure the unambiguity of the data linking in the (semantic) Web. We present the publication of DisGeNET nanopublications as a new Linked Dataset implemented in combination of the Trusty URIs approach. DisGeNET is a database of human genedisease associations from expert-curated databases and text-mining the scientific literature. With a series of illustrative queries we demonstrate its utility.

show abstract

Bio2RDF Release 2: Improved Coverage, Interoperability and Provenance of Life Science Linked Data

Cited by 108 publications

References 17 publications

Data Science and symbolic AI: Synergies, challenges and opportunities

Data Science and symbolic AI: Synergies, challenges and opportunities

Interlinking Scientific Data on a Global Scale

Publishing DisGeNET as nanopublications

Contact Info

Product

Resources

About