%0 Report %D 2023 %T The KnowWhereGraph Ontology %A Cogan Shimizu %A Shirly Stephen %A Kitty Currier %A Pascal Hitzler %A Rui Zhu %A Krzysztof Janowicz %A Mark Schildhauer %A Mohammad Saeid Mahdavinejad %A Abhilekha Dalal %A Adrita Barua %A Ling Cai %A Gengchen Mai %A Zhangyu Wang %A Yuanyuan Tian %A Sanaz Saki Norouzi %A Zilong Liu %A Meilin Shi %A Colby K. Fisher %G eng %0 Conference Paper %D 2023 %T The KnowWhereGraph Ontology: A Showcase %A Cogan Shimizu %A Shirly Stephen %A Rui Zhu %A Kitty Currier %A Mark Schildhauer %A Dean Rehberger %A Pascal Hitzler %A Krzysztof Janowicz %A Colby K. Fisher %A Mohammad Saeid Mahdavinejad %A Antrea Christou %A Adrita Barua %A Abhilekha Dalal %A Sanaz Saki Norouzi %A Zilong Liu %A Meilin Shi %A Ling Cai %A Gengchen Mai %A Zhangyu Wang %A Yuanyuan Tian %G eng %0 Journal Article %J Semantic Web %D 2022 %T Diverse data! Diverse schemata? %A Krzysztof Janowicz %A Cogan Shimizu %A Pascal Hitzler %A Gengchen Mai %A Shirly Stephen %A Rui Zhu %A Ling Cai %A Lu Zhou %A Mark Schildhauer %A Zilong Liu %A Zhangyu Wang %A Meilin Shi %B Semantic Web %V 13 %P 1–3 %G eng %U https://doi.org/10.3233/SW-210453 %R 10.3233/SW-210453 %0 Journal Article %J AI Magazine %D 2022 %T Know, Know Where, KnowWhereGraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence %A Krzysztof Janowicz %A Pascal Hitzler %A Wenwen Li %A Dean Rehberger %A Mark Schildhauer %A Rui Zhu %A Cogan Shimizu %A Colby K. Fisher %A Ling Cai %A Gengchen Mai %A Joseph Zalewski %A Lu Zhou %A Shirly Stephen %A Seila Gonzalez %A Bryce Mecum %A Anna Lopez Carr %A Andrew Schroeder %A Dave Smith %A Dawn Wright %A Sizhe Wang %A Yuanyuan Tian %A Zilong Liu %A Meilin Shi %A Anthony D’Onofrio %A Zhining Gu %B AI Magazine %G eng %0 Conference Paper %B DaMaLOS 2021 @ ISWC %D 2021 %T Environmental Observations in Knowledge Graphs %A Rui Zhu %A Shirly Stephen Ambrose %A Lu Zhou %A Cogan Shimizu %A Ling Cai %A Gengchen Mai %A Krzysztof Janowicz %A Pascal Hitzler %A Mark Schildhauer %X

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. 

%B DaMaLOS 2021 @ ISWC %G eng %0 Conference Paper %B The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand %D 2021 %T SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology %A Rui Zhu %A Cogan Shimizu %A Shirly Stephen %A Lu Zhou %A Ling Cai %A Gengchen Mai %A Krzysztof Janowicz %A Mark Schildhauer %A Pascal Hitzler %B The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand %I ACM %G eng