# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 2 Related Work  
State-of-the-art solutions propose different methods to address the three key problems of semantic publishing: (1) what metadata should be included, (2) how this metadata is modelled, and (3) how to store and access to this metadata. For instance, SciGraph ([[https://scigraph.springernature.com](https://scigraph.springernature.com)]([https://scigraph.springernature.com](https://scigraph.springernature.com))), created by Springer-Nature, address problem 1 by providing the most common metadata for scientific literature, including authors, affiliations, references or publishers, among others. The abstract of the articles is the only textual content taken into account. In connection with problem 2, this project uses an RDF model based in known vocabularies, such as dcterms, foaf or schema.org [13], in addition to own ontologies. Although semantic enrichment is not performed, each article is classified according Field of research (FoR) codes [14]. Regarding problem 3, the knowledge graph itself is not directly accessible by using any query language. Nonetheless, raw data is provided through JSON-LD [15] files. Also, there is a REST API available, which provides functionality for a literature search, supporting metadata-based filters, including DOI, FoR code and year of publication or journal, among others.  
Other related work is _The Semantic Lancet project_[16]. This work proposes a model compliant with the _Semantic Publishing And Referencing Ontologies_ (SPAR ontologies) [17], which include common bibliographic metadata. In this case, the content of the articles is taken into account for semantic enriching purposes, particularly, processing the abstracts by means of FRED tool [18] to obtain an RDF representation that captures the meaning of the free text. This project proposes a triplestore in order to publish the knowledge graph, making it accessible through an SPARQL endpoint. Moreover, they provide several applications built on the top of the knowledge graph, which offer an easy to use user interface.  
Another recent project is the _Open Research Knowledge Graph_ (ORKG) [19], whose model represents not only bibliographic metadata but also semantic descriptions of scholarship knowledge. The semantic enrichment in this model consists of describing each article by means of four generic concepts that summarize its contribution: process, method, material and data. Then, concrete annotations found in the text are linked to these generic concepts. Regarding how to store the data, this project makes use of a labelled property graph, a triplestore and a relational database, where each database engine is used for specific purposes in order to provide different services.  
OpenBiodiv [20] is another knowledge graph focused on biodiversity in the scientific literature. It is defined by the OpenBiodiv-O ontology [21], which integrates bibliographic and biodiversity domain ontologies, such as SPAR ontologies or Treatment Ontology ([[https://github.com/plazi/TreatmentOntologies](https://github.com/plazi/TreatmentOntologies)]([https://github.com/plazi/TreatmentOntologies](https://github.com/plazi/TreatmentOntologies))). This model is defined in RDF, and it includes information about article metadata, authors, institutions, geographical locations or taxon names, among others. Although the content of the articles is also included in this model, it is not processed by any semantic enriching process. This knowledge graph is implemented in a triplestore, and accessible through an SPARQL endpoint.