A formalization of one of the main claims of “OpenBiodiv: A knowledge graph for literature-extracted linked open data in biodiversity science” by Penev et al. 2019 1

1. Introduction

Penev et al. [2] state that “OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone.”. We present here a formalization of the main scientific claim from this quote by using a semantic template called the super-pattern [1].

2. Formalization

Our formalization looks as follows:

In the context class we use a new minted class “release of OpenBiodiv knowledge graph” that is a subclass of “software release” (Q20631656) from Wikidata and has a related class a new minted class “OpenBiodiv knowledge graph” that is a subclass of the “knowledge graph” (Q33002955) class from Wikidata. In the subject class, we use a new minted class “triple in OpenBiodiv knowledge graph” that is related to a new minted class “OpenBiodiv knowledge graph” that is a subclass of the “knowledge graph” (Q33002955) class from Wikidata. In the object class we minted a new class “semantic triple extracted from biodiversity literature” that is related to a new minted class “OpenBiodiv knowledge graph” that is a subclass of the “knowledge graph” (Q33002955) class from Wikidata.

3. RDF code

This is our formalization as a nanopublication in TriG format:

The following nanopublications introduce the newly minted classes in TriG format.

This is the class definition of “release of OpenBiodiv knowledge graph”:

This is the class definition of “triple in OpenBiodiv knowledge graph”:

This is the class definition of “semantic triple extracted from biodiversity literature”:

This is the class definition of “OpenBiodiv knowledge graph”:

Funding

This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement IGNITE (No. 764840).