# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 4 Results
### The Knowledge Graph
#### 4.2.2 Analysis of the Annotations Set  
305,853 annotations were produced for the 127 articles (see Query 12). We can extract the most common annotations produced in the whole data set by querying the knowledge graph (see Query 13). These annotations include general domain terms such as PLANTS, STRESS, RICE, or SOIL among others (see Table 1). On the other hand, the query for the least frequent annotations (see Query 14) returns that 5742 annotations appear only once in the data set (see Query 15). Table 2 shows an example set of ten such annotations, most of them referring to concrete concepts such as 1-METHYLCYCLOPROPENE or ABSCISIC ACID METABOLISM. The specificity of a concept can be measured through its depth in the corresponding ontology. Hence, we can obtain (see Query 16) for each annotation, the number of classes that are supporting the annotation, and the average depth of such classes in the hierarchy of their source ontologies. The most frequent annotations are associated with general concepts, whereas the least frequent ones are more specific. Correspondingly, the most frequent annotations are supported, in general, by ontology classes with smaller average depth (see Table 3). We can extract data about which ontologies are mostly represented in the annotations set. For example, Table 4 shows the ten largest ontologies together with the number of ontology entities related with annotations, the total number of entities in that ontology, and the percentage of ontology usage in annotation, which results of the division between these two previous values (see Query 17). For this purpose, a Neo4j plugin for URI management was developed ([[https://github.com/fanavarro/URIManager](https://github.com/fanavarro/URIManager)]([https://github.com/fanavarro/URIManager](https://github.com/fanavarro/URIManager))) to distinguish between the ontology base URI and the local identifier for an entity; for instance, the URI [[http://purl.obolibrary.org/obo/GO_0003904](http://purl.obolibrary.org/obo/GO_0003904)]([http://purl.obolibrary.org/obo/GO_0003904](http://purl.obolibrary.org/obo/GO_0003904)) is split into [[http://purl.obolibrary.org/obo/GO](http://purl.obolibrary.org/obo/GO)]([http://purl.obolibrary.org/obo/GO](http://purl.obolibrary.org/obo/GO)) (ontology base URI) and 0003904 (entity identifier). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Annotation** & **Occurrences** & **Annotated Articles** \\ \hline PLANTS & 10,960 & 109 \\ PLANT & 3656 & 112 \\ STRESS & 2365 & 74 \\ SOIL & 2334 & 88 \\ RICE & 1883 & 65 \\ EXPRESSION & 1683 & 70 \\ GROWTH & 1465 & 106 \\ OTHER & 1311 & 126 \\ STUDY & 1286 & 118 \\ GENES & 1283 & 72 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ten most common annotations in the whole data set, their number of occurrences and the number of articles annotated with them, sorted by the number of occurrences. \begin{table}
\begin{tabular}{c} \hline \hline
**Annotation** \\ \hline
1-METHYLCYCLOPROPENE \\
1-PROPANOL \\ ABA RESPONSE \\ ABSCISIC ACID METABOLISM \\ ACETYLATION \\ ACR2 \\ BOTANICAL GARDEN \\ BREAST ADENOCARCINOMA \\ CHAPERONIN \\ SESAME OIL \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of ten annotations that only appear once in the data set. An advantage of having a knowledge graph supported by ontologies is that the hierarchy of the ontology, defined through subClassOf edges in the graph, can be exploited for improving the quality of the results of queries. For instance, if we are interested in articles about polymers, not only articles annotated with POLYMERS would be retrieved but also those annotated with types of polymers. This is exemplified in Query 18, where we use the ontology PO2_DG to obtain all the subclasses of polymers class, and then we search all articles annotated with these classes. The results of this query include articles annotated with POLYMERS, POLYACRYLAMIDE or TEFLON, among others.