Decentralized geospatial metadata management

Fugazza, Cristiano; d’Aragona, Paolo Tagliolato Acquaviva; Oggioni, Alessandro; Carrara, Paola

doi:10.1007/s12145-020-00559-1

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This enables the discovery of the semantics between word patterns that allows the identification of relevant resources not directly containing the query terms. Fugazza et al [16] and Fugazza et al [17] propose a methodology to add semantic features to metadata that allows metadata delegation and facilitates the identification of relations and simplifies their evolution management. Miao et al [18] show how to improve the effectiveness of geospatial data discovery using a measurement model of spatio-temporal similarity.…”

Section: Related Workmentioning

confidence: 99%

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

Lacasta

López-Pellicer

Zarazaga‐Soria

et al. 2022

IJGI

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The 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.

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Section: Related Workmentioning

confidence: 99%

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

Lacasta

López-Pellicer

Zarazaga‐Soria

et al. 2022

IJGI

View full text Add to dashboard Cite

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Enabling the Reuse of Long-Term Marine Biological Observations in Essential Variables Frameworks Through a Practical Approach

et al. 2021

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Essential Biodiversity Variables (EBVs) and Biological and Ecosystem Essential Ocean Variables (BioEco EOVs) are two cooperative conceptual frameworks which help harmonize and process multi-source marine biodiversity observations into robust indexes, in order to measure progress toward policy conservation goals. Long-term monitoring networks are encouraged to contribute to these frameworks by mobilizing historical times series which are suitable for detecting impacts of management policies. In this paper, we identify specific recommendations for increasing reuse in the EV frameworks of the biodiversity historical data collected and maintained by the Gulf of Venice (GOV) site, i.e., the monitoring facility that is selected as case study in the Italian Long-Term Ecological Research network (LTER-Italy). The recommendations are obtained through a practical approach comprising two phases. In the first phase, a literature review helps extract the guidelines for implementing the principles representing the most recent attempt to unify management of EBV and BioEco EOV data, i.e., Benson’s tenets. In the second phase, we compare the guidelines to the data management practices enacted by the selected monitoring site in order to recommend curation interventions. The outputs of the analysis are discussed in order to verify if the approach and the recommendations are general enough to be replicated in the marine component of monitoring networks to coordinate the LTER data contribution to the EV frameworks.

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Decentralized geospatial metadata management

Cited by 2 publications

References 14 publications

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

Enabling the Reuse of Long-Term Marine Biological Observations in Essential Variables Frameworks Through a Practical Approach

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