## 1 Introduction  
Presently, geographic positioning and mobility information play a key role in the field of big data analysis. Indeed, several analytical tools and methodologies have been successfully applied to these data in order to extract knowledge in many fields, such as road traffic, human mobility, and currency circulation. An example of one of these techniques, which has been adopted to estimate the typical distance traveled by an individual (or to accomplish other tasks in the context of mobility), is the Radius of Gyration (RoG) [1, 2, 3]. Despite the above mentioned analyses enabling researchers to discover relevant insights and make contribution in several scientific domains, they, however, have been rarely applied outside the geographic realm. Indeed, we have not found many works in the literature concerning the application of mobility and geographic techniques outside such scenarios, although they, ideally, can be adopted to retrieve information also in other fields, whenever an appropriate form of a geometric space is given. An interesting field, in which mobility techniques have never been applied, to the best of our knowledge, is the semantic realm. In this context, the usage of the so-called _word embeddings_, usually based on neural networks, such as Word2 vec [4] or BERT [5], allows encoding relevant features for text classification and mining into vectors of a multi-dimensional space. More precisely, such algorithms are fed with a corpus of sentences in order to create a model (i.e., semantic or embedding space) in which semantically related words are mapped to nearby points. In the resulting semantic space, the notion of proximity between two points (i.e., the multi-dimensional vectors) reflects a similarity relation among terms, and thus it is possible to cluster similar words into topics. For this reason, geographic techniques can be used to extract knowledge with a mobility perspective. This means that is possible to analyze how the authors virtually travel along the topics' space. This last idea, i.e., to exploit the vector space obtained with word embedding based on neural network looking for latent trajectories, has been poorly investigated in the literature. To address this intriguing goal, we consider the world of scientific research activities, traced by scholarly articles, as a paradigmatic case of study, since they often represent a notable and exciting raw material for data analysis. In particular, the proposed case study tracks the papers of the world's most highly cited researchers (according to Clarivate Analytics), obtained by systematic queries on Scopus [6], which is a well-known and vast repository of metadata related to scientific works. Moreover, we compared the embeddings obtained training the Word2 vec algorithm and the ones using a pre-trained Siamese BERT called SBERT that is a modification of the pretrained BERT network that uses siamese and triplet network structures to derive semantically meaningful sentence embeddings [7]. We used SBERT rather than a single BERT because of the computational efficiency demonstrated from this architecture to compute the embeddings. We applied the two embedding learning models to a large corpus of abstracts and keywords of scholarly articles in order to build a semantic space. In the obtained model, the semantic mobility of authors across different research topics can be estimated with analytical tools traditionally applied on geographic data. In particular, starting from the _Clarivate Analytics highly cited researchers_ list, we select a group of authors and a subset of their papers. For each author \(\alpha\) and year \(\gamma\) in the time interval [2005, 2015], the embedding algorithms are fed with words contained in the abstract and keywords of the author's papers published in year \(\gamma\). This way, we generate a sequence of points in the semantic space, where each point is associated with one year of the proposed interval of time and represents the author \"average topic\" for the related year.