# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 4 Results
### Use Case 1: Similarity between Articles  
An important feature in scientific literature repositories is the ability to search articles that are similar to a given one. We can use the annotations of the articles for comparing them so that a higher number of common annotations between two articles entails a higher degree of similarity. In this work, the similarity between two articles is calculated as shown in Equation 1, where \(A\) and \(B\) are the sets of annotations associated with the two articles. The implementation of this algorithm is available in the Neo4j library _Graph Algorithms (algo)_ ([[https://Neo4j.com/docs/graph-algorithms](https://Neo4j.com/docs/graph-algorithms)]([https://Neo4j.com/docs/graph-algorithms](https://Neo4j.com/docs/graph-algorithms))).  
Figure 6: Number of articles per journal.  
Figure 7: Number of articles per publisher.  
\[O(A,B)=\frac{\mid A\cap B\mid}{min(\mid A\mid,\mid B\mid)} \tag{1}\]  
The most similar articles are _Potato Annexin STANN1 Promotes Drought Tolerance and Mitigates Light Stress in Transgenic Solanum tuberosum L. Plants_ (pmid = 26172952) and _Expression of miR159 Is Altered in Tomato Plants Undergoing Drought Stress_ (pmid = 31269704), with a similarity of 0.69. In contrast, the lowest similarity (0.14) is reached for the articles _Tolerance of Transplastomic Tobacco Plants Overexpression a Theta Class Glutathione Transferase to Abiotic and Oxidative Stresses_ (pmid = 30687339) and _Folk use of medicinal plants in Karst and Gorjanci, Slovenia._ pmid = 28231793 (see Query 19). The clustered heatmap in Figure 8, obtained with R [38] and gplots [39], shows the similarity between all articles and a histogram indicating the similarity distribution (note that not all article labels are included in the figure due to readability reasons).  
We analyzed the underlying clusters resulting from the heatmap calculation. We used the _dendextend_ R package [40] to estimate the optimal number of clusters \(k\), by applying the average silhouette width method [41]. Figure 9 shows that the optimal number of clusters is 2, which is in concordance with using the similarity score for the clustering. The largest cluster includes 94 articles which present similarity between them. The second cluster contains 31 articles with low similarity.  
Finally, we extracted the most significant annotations for related articles from the cluster. For this, we generated a data set per cluster, which included the annotations of the articles in such cluster and their frequency in the cluster. The annotations appearing in both data sets were removed to keep only cluster-specific annotations. Finally, we can obtain the ranking of annotations representing the cluster by sorting them by frequency. Figure 10 shows a word cloud graph, obtained with _wordcloud_ R package [42], including the forty most frequent annotations in the cluster of articles with similar documents.  
Figure 8: Heatmap showing the similarity between all articles in the data set, together with an histogram indicating how many article pairs exist with a concrete similarity.