01726nas a2200193   4500008004100000245005100041210005100092520112900143100001301272700002901285700001301314700001901327700001401346700001801360700002401378700002001402700002201422856008801444       2021                        eng d00aEnvironmental Observations in Knowledge Graphs0 aEnvironmental Observations in Knowledge Graphs3 a<p>The notion of Linked Open Science rests on the assumption that Linked Data principles contribute to science and scientific data management in several distinct ways (e.g., by adding rich semantics to improve retrieval and reuse of data). This begs the question of the right level of granularity for such semantic enrichment. On the one extreme of the spectrum, one may provide semantic annotations on the level of entire datasets to improve retrieval while leaving the actual data untouched. On the other end, one may semantically describe every single datum, such as a particular observation leading to data that supports reasoning, automated conflation, and so on, while, at the same time, dramatically increasing the size of data, including redundancy. This paper reports on our experience in modeling heterogeneous environmental data using a semantically-enabled observation framework, namely the SOSA ontology and its extensions to handle observation collections. We discuss different means of using these observation collections and compare their pros and cons in terms of data size and ease of querying.&nbsp;</p>
1 aZhu, Rui1 aAmbrose, Shirly, Stephen1 aZhou, Lu1 aShimizu, Cogan1 aCai, Ling1 aMai, Gengchen1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aSchildhauer, Mark  uhttps://daselab.cs.ksu.edu/publications/environmental-observations-knowledge-graphs