Knowledge-as-a-Service: A Community Knowledge Base for Research Infrastructures in Environmental and Earth Sciences

Zhao, Zhiming; Liao, Xiaofeng; Martin, Paul; Maduro, Jordan; Thijsse, Peter; Schaap, Dick; Stocker, Markus; Goldfarb, Doron; Magagna, Barbara

doi:10.1109/services.2019.00041

Cited by 10 publications

(13 citation statements)

References 12 publications

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“…We developed Open Information Linking for Environmental Research Infrastructures (OIL-E, described more completely in Chapter 6) to capture the stereotypical elements of environmental and Earth science RIs as identified by ENVRI RM, and define the necessary relationships between those stereotypes across different views of science, information, computation, engineering and technology. One of the roles of OIL-E, aside from allowing for various RI descriptions based on ENVRI RM to be transformed into a format that can be uploaded into an ENVRI Knowledge Base [39] and programmatically queried, was to act as a connective 'hub' ontology for RI architecture. This ontology allows specifications of specific concepts to be extended with other, more specific ontologies and taxonomies used by the scientific community.…”

Section: Semantic Linking In Envriplusmentioning

confidence: 99%

Semantic Linking of Research Infrastructure Metadata

Martin

Magagna

Liao

et al. 2020

Lecture Notes in Computer Science

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The use of metadata to characterise scientific datasets, making data easier to discover and use directly by researchers and via various online data services, is one of the primary concerns of research infrastructures (RIs); also, of concern is the use of metadata to describe equipment, facilities, services and other research assets. Metadata models and terminology differ greatly between different communities and infrastructures however, and so make synthesising complex interdisciplinary scientific workflows involving assets from multiple RIs very challenging.'Semantic linking' addresses the need to enhance the interoperability of RI services and data by bridging metadata schemes, ontologies and vocabularies used by different research communities, whether by standardising the terminologies and schemes used by those communities, or by dynamically transforming metadata from one standard to another when retrieved by services on behalf of researchers executing their scientific workflows.Multiple techniques for and modes of semantic linking have been investigated in the context of the ENVRI community cluster of environmental and Earth science RIs, including top-down modelling of entities and activities within a standard reference model, enrichment of existing metadata records with shared terminology, full transformation of metadata records from one standard to another, and the generation of additional links to existing online data. We review some of these activities and their application to the promotion of semantic interoperability between RIs, and discuss other possibilities and recent developments that may also be useful for enhancing interdisciplinary data science.

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Section: Semantic Linking In Envriplusmentioning

confidence: 99%

Semantic Linking of Research Infrastructure Metadata

Martin

Magagna

Liao

et al. 2020

Lecture Notes in Computer Science

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“…The RI status, including architecture and available data management services, the service portfolio, and the FAIRness self-assessment (performed in the ENVRI-FAIR project) have been ingested in the knowledge base. The details of the knowledge base have been discussed in [13] and Chapter 6.…”

Section: A Community Knowledge Basementioning

confidence: 99%

Towards Operational Research Infrastructures with FAIR Data and Services

Zhao

Jeffery²,

Stocker

et al. 2020

Lecture Notes in Computer Science

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Environmental research infrastructures aim to provide scientists with facilities, resources and services to enable scientists to effectively perform advanced research. When addressing societal challenges such as climate change and pollution, scientists usually need data, models and methods from different domains to tackle the complexity of the complete environmental system. Research infrastructures are thus required to enable all data, including services, products, and virtual research environments is FAIR for research communities: Findable, Accessible, Interoperable and Reusable. In this last chapter, we conclude and identify future challenges in research infrastructure operation, user support, interoperability, and future evolution.

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“…Complex systems require support environments that can provide assistance in various aspects, such as data and knowledge storage, process modeling and simulation, and so on. For instance, three main support environments are identified in [17]: (i) e-infrastructure, covering computing, storage, and network capabilities; (ii) research infrastructure, allowing handling of assets from various domains; and (iii) virtual research environments, enabling user-centric support for data selection and discovery. In this work we target some functional aspects from all three categories, namely storage, export of ontologies from different areas, and data selection and discovery mechanisms.…”

Section: Related Literaturementioning

confidence: 99%

Semantic Data Management for a Virtual Factory Collaborative Environment

et al. 2019

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Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.

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Knowledge-as-a-Service: A Community Knowledge Base for Research Infrastructures in Environmental and Earth Sciences

Cited by 10 publications

References 12 publications

Semantic Linking of Research Infrastructure Metadata

Semantic Linking of Research Infrastructure Metadata

Towards Operational Research Infrastructures with FAIR Data and Services

Semantic Data Management for a Virtual Factory Collaborative Environment

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