Liberating links between datasets using lightweight data publishing: an example using plant names and the taxonomic literature

Page, Roderic

doi:10.3897/bdj.6.e27539

Cited by 7 publications

(9 citation statements)

References 24 publications

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Section: Constructing Knowledge Graphsmentioning

confidence: 99%

“…Other efforts to produce domain-specific KGs were published in a variety of areas, including biodiversity (71)(72)(73), the microbiome (74), and for the purpose of enriching clinical data (75,76). The articles on biodiversity focused specifically on how a KG could be created and linked to identifiers in the literature (71,72) or other important biodiversity resources (73). In contrast, papers using KGs for clinical enrichment aimed to use KGs as way to link clinical data to sources of evidence to provide support to clinical observations (76)(77)(78) or to help make the data more interpretable with respect to underlying biological mechanism(s) (75) for improved diagnosis (79).…”

Section: Constructing Knowledge Graphsmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge-Based Biomedical Data Science

Callahan

Tripodi

Pielke-Lombardo

et al. 2020

Annu. Rev. Biomed. Data Sci.

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Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.TBoxes (for terminology), and assertions composed of them are ABoxes (for assertion) (12). Knowledge-base vs. Knowledge GraphKnowledge-bases that can be represented as graphs are often called knowledge graphs.While not all knowledge-bases are implemented as graphs (e.g. some are databases where table structure makes implicit assertions), in recent years, it has become very common to represent knowledge-bases using the Semantic Web standard or, at least be able to produce and consume Semantic Web compatible versions. For that reason, the terms knowledge-base and knowledge graph are often used interchangeably. In 2012, Google announced its proprietary Knowledge Graph, which also popularized the use of the term (13). The literature sometimes contains terminological imprecision about what the differences are between knowledge-bases, knowledge graphs and ontologies; there is a review and analysis of various published definitions (14). In this review, we use the term knowledge graph (or KG) and say a KG is grounded in the set of primitives from which it is constructed. Some KGs also include a set of logical rules that relate assertions to each other (e.g. Human TP53 is the subclass of TP53 proteins that is found in the organism human) called axioms. Biomedical ApplicationsKBDS does computation over KGs (and perhaps other inputs) to make inferences about biomedicine. While each of the publications surveyed below addresses different problems using different techniques, there are some common themes in the computational approaches to using KGs.

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Section: Constructing Knowledge Graphsmentioning

confidence: 99%

Section: Constructing Knowledge Graphsmentioning

confidence: 99%

Knowledge-Based Biomedical Data Science

Callahan

Tripodi

Pielke-Lombardo

et al. 2020

Annu. Rev. Biomed. Data Sci.

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“…As part of ongoing work to match citation strings for taxonomic names to persistent identifiers for the corresponding publications ( [10]), I developed a set of scripts to matching the text string citations to digital identifiers such as DOIs, Handles, JSTOR links, etc. The difficult part of this work is mapping the page-level citations stored in IPNI to work-level bibliographic data.…”

Section: Publicationsmentioning

confidence: 99%

“…However, as yet we don't have a comprehensive bibliography of life ( [6]), hence much of the work in making the mapping involves scouring the web for sources of bibliographic information in the hope that these will include works containing the IPNI citations. This mapping between IPNI names is periodically uploaded to a GitHub repository 4 , and has also been published as a "datasette" [10]. For this project I took this mapping and exported it in RDF.…”

Section: Publicationsmentioning

confidence: 99%

Reconciling author names in taxonomic and publication databases

Page

2019

Preprint

Self Cite

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Taxonomic names remain fundamental to linking biodiversity data, but information on these names resides in separate silos. Despite often making their contents available in RDF, records in these taxonomic databases are rarely linked to identifiers in external databases, such as DOIs for publications, or ORCIDs for people. This paper explores how author names in publication databases such as CrossRef and ORCID can be reconciled with author names in a taxonomic database using existing vocabularies and SPARQL queries. Linking taxonomic namesWe can represent "core" biodiversity data as a network of connected entities, such as taxa and their names, publications, people, species, macromolecular sequences, images, and natural history collections [9]. Creating a "biodiversity knowledge graph" is an implicit goal of several initiatives in biodiversity informatics. Indeed, taxonomic databases were early adopters of the Resource Description Framework (RDF) for describing entities and their interrelationships. From 2005 onwards, major databases of taxonomic names ("nomenclators") for plants, animals, and fungi have used Life Science Identifiers (LSIDs) [2] to uniquely identify those names. LSIDs can be dereferenced to return metadata in RDF [8], and several databases used the same vocabulary (developed by TDWG) to encode information about taxonomic names, their status (e.g., were the names in current use), and where the names were published. The use of globally unique identifiers that can be dereferenced, and which return data in a consistent, machine-readable format would seem to satisfy the preconditions for creating biodiversity knowledge graph [9].Despite the obvious desirability of linking biodiversity data together ([1]), the biodiversity knowledge graph as yet to spontaneously assemble itself. Arguably the biggest reason is that there were few, if any, connections between taxonomic information and external data sources. For example, taxonomic databases typically cite the taxonomic literature using text strings, rather than persistent identifiers. Hence, we still have silos, albeit silos available in linked data formats.

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“…This is just a hint about how exploring Full-size  DOI: 10.7717/peerj-cs.335/fig- 1 the nanopublication relation network could lead to finding related concepts and assertions that might not be explicitly connected in the scientific literature and databases. Nonetheless, despite these premises, nanopublications are not widely used by scientists outside specific circles (Page, 2018); they are hard to find and rarely cited. Nanopublications rarely have a human-readable accessible version and cannot be searched via keywords or natural language queries.…”

Section: Introductionmentioning

confidence: 99%

Search, access, and explore life science nanopublications on the Web

Giachelle¹,

Dosso²,

Silvello³

2021

PeerJ Computer Science

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Nanopublications are Resource Description Framework (RDF) graphs encoding scientific facts extracted from the literature and enriched with provenance and attribution information. There are millions of nanopublications currently available on the Web, especially in the life science domain. Nanopublications are thought to facilitate the discovery, exploration, and re-use of scientific facts. Nevertheless, they are still not widely used by scientists outside specific circles; they are hard to find and rarely cited. We believe this is due to the lack of services to seek, find and understand nanopublications’ content. To this end, we present the NanoWeb application to seamlessly search, access, explore, and re-use the nanopublications publicly available on the Web. For the time being, NanoWeb focuses on the life science domain where the vastest amount of nanopublications are available. It is a unified access point to the world of nanopublications enabling search over graph data, direct connections to evidence papers, and scientific curated databases, and visual and intuitive exploration of the relation network created by the encoded scientific facts.

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Liberating links between datasets using lightweight data publishing: an example using plant names and the taxonomic literature

Cited by 7 publications

References 24 publications

Knowledge-Based Biomedical Data Science

Knowledge-Based Biomedical Data Science

Reconciling author names in taxonomic and publication databases

Search, access, and explore life science nanopublications on the Web

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