## 3 Results
### Crawling Results  
In a baseline setting, automatic crawling would just exhaustively return the references of the seeds and then, recursively, the references of these references. This causes a significant growth in the number of fetched documents without ascertaining the quality of the results. A human, on the other hand, would follow a much more targeted approach by evaluating the most promising documents each time, visiting them, and, in turn, judging their references. In the RL setting, the agent may determine that, in some cases, it is promising to follow a marginally relevant reference to then reach a wealth of other publications that might not have been retrieved with the previous method.  
In this case, we measure the reduction in crawled publications compared to the baseline. We also take into account how many documents retrieved were indeed relevant to our topic. We note that in the baseline approach:  
* in the first 25 documents, we had approximately 850 references to visit;
* in the first 700 fetched documents, the identified references were approximately 10,000.  
We have estimated, by sampling 50 representative documents, that the percentage of related references per document is approximately 19%. On the other hand, our DQN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Families** & **Part of Plant Used** & **Uses/Recipes** & **MeSH Terms** & **Geographical Regions** \\ \hline Asteraceae & Fruit & Poultice & Pharmacological action \\ \hline Boraginaceae & Stem & tsp of oil & Disease \\ \hline Brassicaceae & Bark & Paste & Treatment \\ \hline Cactaceae & Root & \begin{tabular}{c} Whole plant \\ preparation \\ \end{tabular} & Antimicrobial activity \\ \hline Cannabaceae & Clove & Cook & Radical scavenging activity \\ \hline Capparaceae & Stigma & Raw & Antioxidant activity \\ \hline Cistaceae & Bulb & Milk & Ethnobotany \\ \hline Fabaceae & Foliage & \begin{tabular}{c} Solvent/adjuvant \\ used \\ \end{tabular} & Pharmacognosy \\ \hline Fagaceae & Shoot & Honey & Herbal medicine \\ \hline Gentianaceae & Branch & Wine/Water & Greek folk medicine \\ \hline Hypericaceae & Whole Plant & Filtrate & Home remedies \\ \hline Lamiaceae & Wooden & Powarded & Folk remedies \\ \hline Lilliaceae & Kernel & Extract & Materia media \\ \hline Malvaccae & Fiber & Dried & Phytotherapy \\ \hline Moraceae & Rhize & Fresh & Southern Balkans \\ \hline Myrtaceae & Ground plant & Soup & Balkans \\ \hline Oleaceae & Petioles & soaked in & Albanian ethnopharmacology \\ \hline Paeoniaceae & Stem bark & Milled & Bulgarian ethnopharmacology \\ \hline Platatanaceae & Tuberous root & Mixed with & \begin{tabular}{c} Southern Bulgary \\ ethnopharmacology \\ \end{tabular} \\ \hline Rosaceae & Styles & Warm and smoke & FYROM ethnopharmacology \\ \hline Salicaceae & Late & Chew & \
\begin{tabular}{c} Northern Macedonia \\ ethnopharmacology \\ \end{tabular} \\ \hline Scrophulariaceae & Gum & Swallow & Turkish ethnopharmacology \\ \hline Solanaceae & Peels & Bake &
\begin{tabular}{c} Turkish coastal zone \\ ethnopharmacology \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._agent retrieved 700 documents, with the HR measuring 60% (420 relevant documents from 700), i.e., improving the effectiveness over the baseline 3.1 times. Recall that this HR score is also the mean cumulative reward the agent received during the entire crawling (learning) process.  
As a second aspect, we examined the same number (420) of related documents the expert can retrieve in a unit of time. Taking into account the time needed for the expert to annotate a single document, we estimate that they will need a total time of 36 h for this task, which is a rate of 13 relevant documents per hour. The RL-based system achieved a rate of 68 relevant documents per hour through a 7 h crawling task and, thus, has the improved efficiency over the expert of 5.14 times.