## 2 Related Works  
In this section, we propose a literature review following three main directions: Human mobility techniques, word embedding and trajectories representation learning and finally some other works related to the use case of scholars. A part of giving a comparison between our approach and others in the literature, we would like to highlight that to the best of our knowledge this work is the first that propose a methodology to combine trajectories in a semantic realm and human mobility techniques to perform directly knowledge extraction. Since data are being produced and collected at increasing volumes, automatic knowledge discovery and data mining are becoming more and more important. In particular, in the context of Big Data mining, the ability to retrieve information, insights and values from data, in its heterogeneous forms, is crucial. This task represents a challenge since decades, with the aim to find a trade-off between automatic knowledge discovery and performance of tools and techniques able to give a productive representation of knowledge. In [8], the authors already discussed the need for computational theories and tools to assist humans in the extraction of useful information from the rapidly growing volumes of digital data. Several attempts were presented in the literature in different domains, with a particular attention on ways to extract information from text data exploiting semantic properties. For example, in [9], the authors analyzed healthcare scientific publications to understand directly which methodologies are most used in a field and what is the status of research reached to help stakeholder to plan research funding. Recently, two other research works highlighted the importance of exploiting neural network embedding techniques to extract new knowledge: In [10], [PERSON] et al. presented interesting results in terms of knowledge extraction by applying word2 vec to the analysis of the materials science scientific literature. They demonstrate that embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, they demonstrated that an unsupervised method can recommend materials for functional applications several years before their discovery. Finally, in [11], the authors exploited different word embedding techniques to discover latent information in the financial reports of companies traded in the US stock market. They demonstrated that neural network embedding techniques outperform other traditional methods when applied to track document similarity and changes in their reports associated with abnormal returns in the stock exchange. Neural network embedding techniques are gaining always more attention also for their ability of encoding non-euclidean structures like networks [12], knowledge graphs and dynamic graphs [13] where random walks among nodes are treated as documents where the words are represented by the network's vertices. These techniques on graphs can be applied to analyze big data collections for knowledge discovery (i.e., drug discovery [14], protein classification [15], finance [16], Social Network Analysis [17]). At the same time, the study of human mobility in terms of trajectories has become very important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling [2; 3]. Mobility is often estimated using the so-called Radius of Gyration (RoG), which is a measure of mobility volume and indicates the characteristic distance traveled by an individual [1]. In [18], the Radius of Gyration is used to characterize human mobility patterns emerging from available GPS trajectories. In [19], starting from nation-wide mobile phone data, authors investigate the correlations between the Radius of Gyration and external socio-economic indicators. In light of this, the idea of combining tools from human mobility into knowledge extraction from text is a promising research direction.