@article{fdi:010092365,
  title = {{E}nvironmental and life sciences observations in knowledge graphs using {NLP} techniques to support multidisciplinary studies},
  author = {{A}rslan, {M}. and {D}esconnets, {J}ean-{C}hristophe and {M}ougenot, {I}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he understanding of environmental observations is a continuous challenge for environmental and life science investigations. {T}he environmental data is complex as it involves its own features, methods, properties, systems, and spatio-temporal dimensions. {T}he time granularity remains approximately the same for different environmental contexts but geographic and rest of the above-mentioned entities are defined using domain vocabularies that are specific for each discipline. {I}t is time-consuming for the researchers of life sciences' discipline to discover, access,




and analyze relevant environmental observations as each discipline has its data formats, vocabularies, and metadata standards. {T}hese differences introduce structural and semantic heterogeneities, resulting in creating a barrier for reusing datasets generated by other disciplines. {E}xisting dataset discovery platforms contain domain-specific metadata descriptions for explaining datasets which limits their usage. {T}o overcome this knowledge barrier, this work reports the proof-of-concept implementation of a knowledge graph that is centered towards the oceanography use case scenario using {NLP} techniques (named entity recognition ({NER}) followed by text preprocessing). {T}he constructed knowledge graph is a collection of subgraphs each representing the metadata of a dataset. {I}t uses the geo-spatial and open semantic data standards that aim to provide enhanced metadata descriptions of datasets for enabling multidisciplinary research.},
  keywords = {},
  booktitle = {},
  journal = {{P}rocedia {C}omputer {S}cience},
  volume = {201},
  numero = {},
  pages = {543--550},
  ISSN = {1877-0509},
  year = {2022},
  DOI = {10.1016/j.procs.2022.03.070},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092365},
}