scholarly journals Towards a biodiversity knowledge graph

2016 ◽  
Vol 2 ◽  
pp. e8767 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Knowledge Graph ◽  
Biodiversity Knowledge

scholarly journals The Pensoft Data Publishing Workflow: The FAIRway from articles to Linked Open Data

2019 ◽  
Vol 3 ◽  
Author(s):  
Lyubomir Penev ◽  
Teodor Georgiev ◽  
Viktor Senderov ◽  
Mariya Dimitrova ◽  
Pavel Stoev
Keyword(s):  
Open Data ◽  
Structured Data ◽  
Linked Open Data ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Supplementary File ◽  
Biodiversity Data ◽  
Text Format ◽  
Biodiversity Knowledge ◽  
Data Elements

As one of the first advocates of open access and open data in the field of biodiversity publishiing, Pensoft has adopted a multiple data publishing model, resulting in the ARPHA-BioDiv toolbox (Penev et al. 2017). ARPHA-BioDiv consists of several data publishing workflows and tools described in the Strategies and Guidelines for Publishing of Biodiversity Data and elsewhere: Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. The above mentioned approaches are supported by a whole ecosystem of additional workflows and tools, for example: (1) pre-publication data auditing, involving both human and machine data quality checks (workflow 2); (2) web-service integration with data repositories and data centres, such as Global Biodiversity Information Facility (GBIF), Barcode of Life Data Systems (BOLD), Integrated Digitized Biocollections (iDigBio), Data Observation Network for Earth (DataONE), Long Term Ecological Research (LTER), PlutoF, Dryad, and others (workflows 1,2); (3) semantic markup of the article texts in the TaxPub format facilitating further extraction, distribution and re-use of sub-article elements and data (workflows 3,4); (4) server-to-server import of specimen data from GBIF, BOLD, iDigBio and PlutoR into manuscript text (workflow 3); (5) automated conversion of EML metadata into data paper manuscripts (workflow 2); (6) export of Darwin Core Archive and automated deposition in GBIF (workflow 3); (7) submission of individual images and supplementary data under own DOIs to the Biodiversity Literature Repository, BLR (workflows 1-3); (8) conversion of key data elements from TaxPub articles and taxonomic treatments extracted by Plazi into RDF handled by OpenBiodiv (workflow 5). These approaches represent different aspects of the prospective scholarly publishing of biodiversity data, which in a combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, lay the ground of an entire data publishing ecosystem for biodiversity, supplying FAIR (Findable, Accessible, Interoperable and Reusable data to several interoperable overarching infrastructures, such as GBIF, BLR, Plazi TreatmentBank, OpenBiodiv and various end users.

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

2018 ◽  
Author(s):  
Roderic D. M. Page
Keyword(s):  
Web Server ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Biodiversity Knowledge ◽  
Plant Names ◽  
Cross Links ◽  
Minimal Effort

AbstractConstructing a biodiversity knowledge graph will require making millions of cross links between diversity entities in different datasets. Researchers trying to bootstrap the growth of the biodiversity knowledge graph by constructing databases of links between these entities lack obvious ways to publish these sets of links. One appealing and lightweight approach is to create a “datasette”, a database that is wrapped together with a simple web server that enables users to query the data. Datasettes can be packaged into Docker containers and hosted online with minimal effort. This approach is illustrated using a dataset of links between globally unique identifiers for plant taxonomic names, and identifiers for the taxonomic articles that published those names.

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

2018 ◽  
Vol 6 ◽  
pp. e27539 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Web Server ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Biodiversity Knowledge ◽  
Plant Names ◽  
Cross Links ◽  
Minimal Effort

Constructing a biodiversity knowledge graph will require making millions of cross links between diversity entities in different datasets. Researchers trying to bootstrap the growth of the biodiversity knowledge graph by constructing databases of links between these entities lack obvious ways to publish these sets of links. One appealing and lightweight approach is to create a "datasette", a database that is wrapped together with a simple web server that enables users to query the data. Datasettes can be packaged into Docker containers and hosted online with minimal effort. This approach is illustrated using a dataset of links between globally unique identifiers for plant taxonomic namesand identifiers for the taxonomic articles that published those names.

scholarly journals Ozymandias: A biodiversity knowledge graph

2018 ◽  
Author(s):  
Roderic D. M. Page
Keyword(s):  
Shared Knowledge ◽  
Knowledge Graph ◽  
Web Interface ◽  
Biodiversity Data ◽  
Knowledge Space ◽  
Link Type ◽  
Biodiversity Knowledge

AbstractEnormous quantities of biodiversity data are being made available online, but much of this data remains isolated in their own silos. One approach to breaking these silos is to map local, often database-specific identifiers to shared global identifiers. This mapping can then be used to construct a knowledge graph, where entities such as taxa, publications, people, places, specimens, sequences, and institutions are all part of a single, shared knowledge space. Motivated by the 2018 GBIF Ebbe Nielsen Challenge I explore the feasibility of constructing a “biodiversity knowledge graph” for the Australian fauna. These steps involved in constructing the graph are described, and examples its application are discussed. A web interface to the knowledge graph (called “Ozymandias”) is available at https://ozymandias-demo.herokuapp.com.

scholarly journals Strategies for Assembling the Biodiversity Knowledge Graph

2020 ◽  
Vol 4 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Life Sciences ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Supporting Evidence ◽  
Domain Specific ◽  
Biodiversity Knowledge ◽  
Comprehensive Knowledge ◽  
International Image ◽  
Knowledge Graphs ◽  
Semantic Publishing

This talk explores different strategies for assembling the “biodiversity knowledge graph” (Page 2016). The first is a centralised, crowd-sourced approach using Wikidata as the foundation. Wikidata is becoming increasingly attractive as a knowledge graph for the life sciences (Waagmeester et al. 2020), and I will discuss some of its strengths and limitations, particularly as a source of bibliographic and taxonomic information. For example, Wikidata’s handling of taxonomy is somewhat problematic given the lack of clear separation of taxa and their names. A second approach is to build biodiversity knowledge graphs from scratch, such as OpenBioDiv (Penev et al. 2019) and my own Ozymandias (Page 2019). These approaches use either generalised vocabularies such as schema.org, or domain specific ones such as TaxPub (Catapano 2010) and the Semantic Publishing and Referencing Ontologies (SPAR) (Peroni and Shotton 2018), and to date tend to have restricted focus, whether geographic (e.g., Australian animals in Ozymandias) or temporal (recent taxonomic literature, OpenBioDiv). A growing number of data sources are now using schema.org to describe their data, including ORCID and Zenodo, and efforts to extend schema.org into biology (Bioschemas) suggest we may soon be able to build comprehensive knowledge graphs using just schema.org and its derivatives. A third approach is not to build an entire knowledge graph, but instead focus on constructing small pieces of the graph tightly linked to supporting evidence, for example via annotations. Annotations are increasingly used to mark up both the biomedical literature (e.g., Kim et al. 2015, Venkatesan et al. 2017) and the biodiversity literature (Batista-Navarro et al. 2017). One could argue that taxonomic databases are essentially lists of annotations (“this name appears in this publication on this page”), which suggests we could link literature projects such as the Biodiversity Heritage Library (BHL) to taxonomic databases via annotations. Given that the International Image Interoperability Framework (IIIF) provides a framework for treating publications themselves as a set of annotations (e.g., page images) upon which other annotations can be added (Zundert 2018), this suggests ways that knowledge graphs could lead directly to visualising the links between taxonomy and the taxonomic literature. All three approaches will be discussed, accompanied by working examples.

scholarly journals Training and hackathon on building biodiversity knowledge graphs

2019 ◽  
Vol 5 ◽  
Author(s):  
Joel Sachs ◽  
Roderic Page ◽  
Steven J Baskauf ◽  
Jocelyn Pender ◽  
Beatriz Lujan-Toro ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Graph Database ◽  
Biodiversity Informatics ◽  
Biodiversity Data ◽  
Specific Knowledge ◽  
Biodiversity Knowledge ◽  
Training Event ◽  
Web Infrastructure ◽  
Ongoing Development ◽  
Knowledge Graphs

Knowledge graphs have the potential to unite disconnected digitized biodiversity data, and there are a number of efforts underway to build biodiversity knowledge graphs. More generally, the recent popularity of knowledge graphs, driven in part by the advent and success of the Google Knowledge Graph, has breathed life into the ongoing development of semantic web infrastructure and prototypes in the biodiversity informatics community. We describe a one week training event and hackathon that focused on applying three specific knowledge graph technologies – the Neptune graph database; Metaphactory; and Wikidata - to a diverse set of biodiversity use cases.We give an overview of the training, the projects that were advanced throughout the week, and the critical discussions that emerged. We believe that the main barriers towards adoption of biodiversity knowledge graphs are the lack of understanding of knowledge graphs and the lack of adoption of shared unique identifiers. Furthermore, we believe an important advancement in the outlook of knowledge graph development is the emergence of Wikidata as an identifier broker and as a scoping tool. To remedy the current barriers towards biodiversity knowledge graph development, we recommend continued discussions at workshops and at conferences, which we expect to increase awareness and adoption of knowledge graph technologies.

scholarly journals Progress in Authority Management of People Names for Collections

2019 ◽  
Vol 3 ◽  
Author(s):  
Quentin Groom ◽  
Chloé Besombes ◽  
Josh Brown ◽  
Simon Chagnoux ◽  
Teodor Georgiev ◽  
...  
Keyword(s):  
Service Providers ◽  
International Workshop ◽  
Knowledge Graph ◽  
Data Mapping ◽  
Global Biodiversity Information Facility ◽  
Integrated Network ◽  
Biographical Information ◽  
Geographically Dispersed ◽  
Biodiversity Knowledge ◽  
German Authority

The concept of building a network of relationships between entities, a knowledge graph, is one of the most effective methods to understand the relations between data. By organizing data, we facilitate the discovery of complex patterns not otherwise evident in the raw data. Each datum at the nodes of a knowledge graph needs a persistent identifier (PID) to reference it unambiguously. In the biodiversity knowledge graph, people are key elements (Page 2016). They collect and identify specimens, they publish, observe, work with each other and they name organisms. Yet biodiversity informatics has been slow to adopt PIDs for people and people are currently represented in collection management systems as text strings in various formats. These text strings often do not separate individuals within a collecting team and little biographical information is collected to disambiguate collectors. In March 2019 we organised an international workshop to find solutions to the problem of PIDs for people in collections with the aim of identifying people unambiguously across the world's natural history collections in all of their various roles. Stakeholders were represented from 11 countries, representing libraries, collections, publishers, developers and name registers. We want to identify people for many reasons. Cross-validation of information about a specimen with biographical information on the specimen can be used to clean data. Mapping specimens from individual collectors across multiple herbaria can geolocate specimens accurately. By linking literature to specimens through their authors and collectors we can create collaboration networks leading to a much better understanding of the scientific contribution of collectors and their institutions. For taxonomists, it will be easier to identify nomenclatural type and syntype material, essential for reliable typification. Overall, it will mean that geographically dispersed specimens can be treated much more like a single distributed infrastructure of specimens as is envisaged in the European Distributed Systems of Scientific Collections Infrastructure (DiSSCo). There are several person identifier systems in use. For example, the Virtual International Authority File (VIAF) is a widely used system for published authors. The International Standard Name Identifier (ISNI), has broader scope and incorporates VIAF. The ORCID identifier system provides self-registration of living researchers. Also, Wikidata has identifiers of people, which have the advantage of being easy to add to and correct. There are also national systems, such as the French and German authority files, and considerable sharing of identifiers, particularly on Wikidata. This creates an integrated network of identifiers that could act as a brokerage system. Attendees agreed that no one identifier system should be recommended, however, some are more appropriate for particular circumstances. Some difficulties have still to be resolved to use those identifier schemes for biodiversity : 1) duplicate entries in the same identifier system; 2) handling collector teams and preserving the order of collectors; 3) how we integrate identifiers with standards such as Darwin Core, ABCD and in the Global Biodiversity Information Facility; and 4) many living and dead collectors are only known from their specimens and so they may not pass notability standards required by many authority systems. The participants of the workshop are now working on a number of fronts to make progress on the adoption of PIDs for people in collections. This includes extending pilots that have already been trialed, working with identifier systems to make them more suitable for specimen collectors and talking to service providers to encourage them to use ORCID iDs to identify their users. It was concluded that resolving the problem of person identifiers for collections is largely not a lack of a solution, but a need to implement solutions that already exist.

scholarly journals Wikidata and the biodiversity knowledge graph

2019 ◽  
Vol 3 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Natural History ◽  
Dna Sequences ◽  
Query Language ◽  
Data Entry ◽  
Knowledge Graph ◽  
Natural History Museums ◽  
Global Knowledge ◽  
Mission Creep ◽  
Domain Specific ◽  
Biodiversity Knowledge

This talk explores the role Wikidata (Vrandečić and Krötzsch 2014) might play in the task of assembling biodiversity information into a single, richly annotated and cross linked structure known as the biodiversity knowledge graph (Page 2016). Initially conceived as a language-independent data store of facts derived from the Wikipedia, Wikidata has morphed into a global knowledge graph, complete with a user friendly interface for data entry and a powerful implementation of the SPARQL query language. Wikidata already underpins projects such as Gene Wiki (Burgstaller-Muehlbacher et al. 2016) and Scholia (Nielsen et al. 2017). Much of the content of Wikispecies is being automatically added to Wikidata, hence many of the entities relevant to biodiversity (such as taxa, taxonomic publications, and taxonomists) well represented in Wikidata, making it even more attractive. Much of the data relevant to biodiversity is widely scattered in different locations, requiring considerable manual effort to collect and curate. Appeals to the taxonomic community to undertake these tasks have not always met with success. For example, the Global Registry of Biodiversity Repositories (GrBio) was an attempt to create a global list of biodiversity repositories, such as natural history museums and herbaria. An appeal by Schindel et al. (2016) for the taxonomic community to curate this list largely fell on deaf ears, and at the time of writing the GrBio project is moribund. Given that many repositories are housed in institutions that are the subject of articles in Wikipedia, many of these repositories already have entries in Wikidata. Hence, rather than follow the route GrBio took of building a resource and then hoping a community will assemble around that resource, we could go to Wikidata where there is an existing community and build the resource there. An impressive example of the potential for this is WikiCite, which initially had the goal of including in Wikidata every article cited in any of the Wikipedias. Taxonomic articles are highly cited in Wikipedia (Nielsen 2007), hence already fall within the remit of WikiCite. Hence Wikidata is a candidate for the “bibliography of life” (King et al. 2011), a database of all taxonomic literature. Another important role Wikidata can play is to define the boundaries of a biodiversity knowledge graph. Entities such as journals, articles, people, museums, and herbaria are often already in Wikidata, hence we can delegate managing that content to the Wikidata community (bolstered by our own contributions), and focus instead on domain-specific entities such as DNA sequences, specimens, etc., or domain specific attributes of those entities if they are already in Wikidata. This means we can avoid the inevitable “mission creep” that bedevils any attempt to link together information from multiple disciplines. These ideas are explored using examples based on content entirely within Wikidata (including entities such as publications, authorship, and natural history collections), as well as approaches that combine Wikidata with external knowledge graphs such as Ozymandias (Page 2018).

scholarly journals OpenBiodiv: an Implementaion of a Semantic System Running on top of the Biodiversity Knowledge Graph

2017 ◽  
Vol 1 ◽  
pp. e20084 ◽  
Author(s):  
Viktor Senderov ◽  
Teodor Georgiev ◽  
Donat Agosti ◽  
Terry Catapano ◽  
Guido Sautter ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Semantic System ◽  
Biodiversity Knowledge
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