Our semantic search should be used to explore a previously unknown research field.
It works best when the user provides us with a sentence or question about a COVID-19
related research field, i.e. "How does COVID-19 affect childrens mental health?".
The search algorithm is able to analyze and generalize
a given query and provide the user with
papers that match the query's topic. It is capable of recognizing connections
between words, e.g. temperature, weather, humidity. These connections are used
to show papers of the overall topics that do not necessarily contain any word that the
user provided.
We use natural language processing techniques
to find correlations between a given query and the content of a paper. For semantic
analysis, we trained a BERT model on our dataset that learned to find similarities
between papers and a given query.
You can find out more details about the project at the
frequently asked questions page.