When you’re finding experts, literature or journal suggestions through Global Campus, our Semantic Search matches text to text. AI models built for these, compare the content of publications or projects to your query and then aggregate those results, for instance, on the level of authors (to find experts) or sources (to find journals).
While this type of matchmaking finds excellent matches, it is hard to easily investigate how the identified experts differ from each other. To help our users navigate our results in a more interpretable fashion, the latest update of Global Campus includes Topic information!
Let’s look at an example: The multifunctional use of an aqueous battery for a high capacity jellyfish robot. The topics on the column to the right of the screen, are the topics that belong to publications similar to your input query. By selecting them, you can find out who in the result list has certain specializations that are interesting for your query.
In this example, we see that some specializations are common, such as Design and Control of Soft Robotic Systems. But if you are specifically interested in the battery aspect of our query, only two authors have the topic of Lithium-Ion battery Management in Electric Vehicles topic attached to their works.
Mapping Topics
The same topics are now also available in the updated Network Graph:
In this overview, authors will be placed close to a topic if they have published many papers on those topics. The graph keeps the co-authorships in mind when plotting the authors, thus providing you with a topical and collaborative overview of the field.
Expertise wheels
Lastly, these topics are also available on an author level through the result list, where clicking the ‘Expertise’ button below the profile of an author will bring up a Topic chart for that author. The colors belong to the top level domains (Life Science, Social Science, Physical Sciences and Health Sciences), and thus the appearance of many different colors gives an indication of the multi-disciplinarity of the corpus of a particular author.
These topics are attributed to the papers, and thus to the authors, by OpenAlex in close collaboration with CWTS. If you would like to know more about the origin and the background of these topics, you can learn more through this whitepaper.
