# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 1 Introduction  
The importance of science in society has significantly increased in the last century. Science has become a big, global, complex, and collaborative effort due to the increase of the resources assigned to science world-wide [1; 2]. This has implied an exponential increase of the scientific production, reaching a growth rate between 8 and 9% from the period since the Second World War [3]. According to Scimago Journal & Country Rank ([[https://www.scimagoj.com/journalrank.php](https://www.scimagoj.com/journalrank.php)]([https://www.scimagoj.com/journalrank.php](https://www.scimagoj.com/journalrank.php))), 2,761,624 articles were published in 2018, and 8,185,591 articles were published from 2015 to 2017. The agricultural and biological sciences constitute around 8% of such production, with 226,310 articles published in 2018 and 659,111 from 2015 to 2017. The availability of a larger base of scientific papers is mostly useful for scientists and science, but this is at the cost of making it difficult to find relevant related research. Since a human being is not usually able to review all these documents, this task should be delegated to software tools, which are able to quickly process large amounts of data. By improving the methods used for literature searches, a scientist could quickly find the most interesting articles, which will augment the efficiency of his/her research work. Scientific publications are usually published in human-friendly formats such as PDF or HTML, whose content is hardly understood by machines. Therefore, the first requisite to use automaticprocesses in order to find relevant articles is that these articles are represented in machine readable formats. The Semantic Web [4] proposes different technologies which can contribute to this goal, namely, OWL ontologies [5], for representing domain knowledge; RDF [6], for describing resources; and SPARQL [7] as a query language. These technologies allow representing domain knowledge by using formal languages, which can be understood by machines, enabling the development of knowledge-based automated tasks. Semantic Web technologies are being adopted by publishers due to those benefits. [PERSON] defines semantic publishing as _anything that enhances the meaning of a published journal article, facilitates its automated discovery, enables its linking to semantically related articles, provides access to data within the article in actionable form, or facilitates integration of data between papers_[8]. This involves enriching articles with machine-readable metadata, allowing the verification of published information and providing the capacity for automated discovery and for summarizing. Thus, there are several key problems in semantic publishing: (1) what metadata should be included, (2) how this metadata is modelled, and (3) how to store and access to this metadata. Our previous studies [9; 10] addressed problems 1 and 2 by proposing an RDF model for scientific literature description that reuses terms from well known domain ontologies and vocabularies, such as _Bibliographic Ontology_ (bib) ([[http://bibindontology.com/](http://bibindontology.com/)]([http://bibindontology.com/](http://bibindontology.com/))), _Dublin Core Terms_ (dcterms) [11] or _Friend Of A Friend Ontology_ (foaf) (www.foaf-project.org/), among others, following the Biotea model. This model covers most publishing information, including authors, references, journals, publishers, the structure and the content of the articles, and semantic annotations that enrich these contents. Moreover, we provided tools for RDF generation from literature represented in Journal Article Tag Suite (JATS) format [12]. In this paper, we address problem 3 by proposing the use of graph databases for storing and querying RDF data generated according to the Biotea model. Thus, in this work, we provide a workflow to populate property graphs including not only scientific literature data, but also semantic annotations and domain knowledge, which facilitate better understanding of the data by automatic agents.