doi:10.3390/ijgi11020087engLacasta, J.López Pellicer, F. J.Zarazaga-Soria, J.Béjar, R.Nogueras-Iso, J.Approaches for the clustering of geographic metadata and the automatic detection of quasi-spatial dataset seriesART-2022-127489The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning.2022http://zaguan.unizar.es/record/11055910.3390/ijgi11020087http://zaguan.unizar.es/record/110559oai:zaguan.unizar.es:110559info:eu-repo/grantAgreement/ES/AEI/PID2020-113353RB-I00info:eu-repo/grantAgreement/ES/DGA/T59-20RISPRS International Journal of Geo-Information 11, 2 (2022), 87[19 pp.]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess