SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology

Zhu, Rui; Shimizu, Cogan; Stephen, Shirly; Zhou, Lu; Cai, Ling; Mai, Gengchen; Janowicz, Krzysztof; Schildhauer, Mark; Hitzler, Pascal

doi:10.1145/3502223.3502235

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we plan to further integrate EVKG with other open KG repositories, such as Wikidata and GeoNames, so that more socioeconomic and environmental challenges beyond electric transportation can be comprehensively addressed. Furthermore, a set of shape constraints can be developed to validate the incoming data to EVKG so as to improve the data quality (Zhu, Shimizu, et al, 2021). Last but not least, new analytical methods that can work on KGs could be studied to advance the discovery of insights from a multidisciplinary context.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…One critical class in this module is ev‐ont:ElectricVehicleRegistrationCollection which indicates the class of a collection of EV registrations that share the same spatial and temporal scope, as well as product information by using properties: ev‐ont:hasSpatialScope, ev‐ont:hasTemporalScope, and ev‐ont:hasProductInfo. The idea of using a class to denote a collection of EV registration collection is inspired by the sosa:ObservationCollection class proposed by the extensions to the Semantic Sensor Network Ontology (Cox, 2018; Zhu, Stephen, et al, 2021). In the following, we will use an instance evr:evregcol_1 of the ev‐ont:ElectricVehicleRegistrationCollection class as an example to demonstrate how to use this module.…”

Section: Electric Vehicle Knowledge Graphmentioning

confidence: 99%

See 1 more Smart Citation

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

Mai

Zhu

et al. 2023

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Electric Vehicle Knowledge Graphmentioning

confidence: 99%

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

Mai

Zhu

et al. 2023

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Sensor, Observation, Sample, and Actuator (SOSA) ontology (Janowicz et al, 2019) is the lightweight core of SSN, in which only the core classes and their corresponding properties are specified. SOSA has been widely applied to domains such as remotely sensed images (Kostovska et al, 2020), smart city (Espinoza-Arias et al, 2019), humanitarian relief (Zhu et al, 2021b), the Internet of Things (Honti and Abonyi, 2019), and environmental intelligence (Zhu et al, 2021a). To enable modeling homogeneous collections of observations, an extension to SSN has been introduced 13 .…”

Section: Semantic Sensor Network Ontology and Its Extensionsmentioning

confidence: 99%

COVID-Forecast-Graph: An Open Knowledge Graph for Consolidating COVID-19 Forecasts and Economic Indicators via Place and Time

Zhu

Janowicz

Mai

et al. 2022

AGILE GIScience Ser.

View full text Add to dashboard Cite

Abstract. The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding its social drivers may be as important as understanding the virus itself. One such social driver is misinformation and distrust in institutions. This is particularly interesting as the scientific process is more transparent than ever before. Numerous scientific teams have published datasets that cover almost any imaginable aspects of COVID-19 during the last two years. However, consistently and efficiently integrating and making sense of these separate data “silos” to scientists, decision makers, journalists, and more importantly the general public remain a key challenge with important implications for transparency. Several types of knowledge graphs have been published to tackle this issue and to enable data crosswalks by providing rich contextual information. Interestingly, none of these graphs has focused on COVID-19 forecasts despite them acting as the underpinning for decision making. In this work we motivate the need for exposing forecasts as a knowledge graph, showcase queries that run against the graph, and geographically interlink forecasts with indicators of economic impacts.

show abstract

SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology

Cited by 2 publications

References 12 publications

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

COVID-Forecast-Graph: An Open Knowledge Graph for Consolidating COVID-19 Forecasts and Economic Indicators via Place and Time

Contact Info

Product

Resources

About