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Published in Claudia Lanza, Semantic Control for the Cybersecurity Domain, 2023
The Knowledge Organization Systems (KOS) constitute a wide range of resources functional to organizing and collecting information belonging to specific areas of study, as affirmed by Hodge (2000:11) [66]: “Knowledge organization systems are used to organize materials for the purpose of retrieval and to manage a collection. A KOS serves as a bridge between the user's information need and the material in the collection. With it, the user should be able to identify an object of interest without prior knowledge of its existence. Whether through browsing or direct searching, whether through themes on a Web page or a site search engine, the KOS guides the user through a discovery process.”
Functional Architectures for Indexing and Keywording
Published in Denise Bedford, Knowledge Architectures, 2020
A managed vocabulary is most often referred to as a controlled vocabulary. Controlled vocabularies are often equated with subject headings, thesauri, and knowledge organization system (KOS) lists. While each of these examples is managed and controlled, they go beyond simple management to add structural relationships and meaning. For this text, we characterize a controlled vocabulary more as a managed vocabulary. What is typically described as a controlled vocabulary, we will characterize as a semantically enhanced structure. Well-designed knowledge architectures must support the simple management of a defined vocabulary and the relationships we may assign to those concepts. It is an essential design consideration because we need to support the harmonization of concepts first and address the harmonization and synthesis of relationships and their meanings. Assigned relationships do not necessarily translate well to different business concepts. We cannot merely adopt or inherit them as defined. The architecture must be able to identify, distinguish, discard, and redefine relationships.
Employing Ontology to Capture Expert Intelligence within GEOBIA: Automation of the Interpretation Process
Published in Raechel A. White, Arzu Çöltekin, Robert R. Hoffman, Remote Sensing and Cognition, 2018
Sachit Rajbhandari, Jagannath Aryal, Jon Osborn, Arko Lucieer, Robert Musk
Knowledge can be formally expressed using a KR language. Description logics (DLs), a family of KR language, can be used; in general, this is viewed as decidable fragments of first-order logics (Krötzsch, Simancik, & Horrocks, 2012). Domain knowledge is thus represented using different KR languages such as Resource Description Framework (RDF), Simple Knowledge Organization System (SKOS), or Web Ontology Language (OWL). RDF defines a data model to describe machine-understandable semantics of data in terms of subject-predicate-object expression, which is commonly known as triples in RDF terminology (Broekstra et al., 2002). SKOS is the World Wide Web Consortium (W3C)–recommended data model and vocabulary to express knowledge organization systems (KOSs) such as thesauri and classification schemes (Baker et al., 2013). OWL is a language for modeling ontologies, which became a W3C recommendation in February 2004 (Bechhofer et al., 2004). The basic elements of OWL ontology are classes, individuals, and properties. Classes are sets of individuals and properties that exist either between individuals or between the object and a data type (Belgiu et al., 2013). In our work, we have used OWL language to develop an ontology necessary for LULC classification.
Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures
Published in International Journal of Digital Earth, 2023
Julia Fischer, Lukas Egli, Juliane Groth, Caterina Barrasso, Steffen Ehrmann, Heiko Figgemeier, Christin Henzen, Carsten Meyer, Ralph Müller-Pfefferkorn, Arne Rümmler, Michael Wagner, Lars Bernard, Ralf Seppelt
A common understanding of terms is crucial for collaboration between project partners. Furthermore, knowledge dissemination to potential users or providers with different background relies on a clear and accessible description of focal terms. Any project-specific system to manage and represent domain-specific knowledge that is used to organize and describe the concepts of a project as for instance vocabularies, taxonomies, or ontologies, should be published in human- and machine-readable form, and its contents should be linked to existing concepts. Finally, if datasets refer to terms or definitions in a certain field of the metadata scheme (e.g. tags or CRS), these fields should always link to proper terms that are available in an openly accessible register. We (i) developed the R-package ontologics (https://cran.r-project.org/web/packages/ontologics) to support scientists in the development and setup of use-case-specific ontologies, whose terms are well-defined, harmonized, and linked to terms in other knowledge organization systems, (ii) developed and published an ontology of land-use/landcover concepts (Ehrmann, Rümmler, and Meyer 2022) (Figure 3), and (iii) published an extendable register (https://geokur-dmp.geo.tu-dresden.de/quality-register) of geodata quality indicators that fosters managing descriptions of quality indicators and providing them for reference in data quality descriptions and assessments.
Investigating the link among ICT, electricity consumption, air pollution, and economic growth in EU countries
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Cosimo Magazzino, Donatella Porrini, Giulio Fusco, Nicolas Schneider
Nonetheless, ICT can play a crucial role in achieving energy efficiency targets, especially in the industry sector (Susam and Hudaverdi Ucer 2019). This is an urgent issue since energy consumption and CO2 emissions quantities may exceed the critical level and induce direct externalities on human health and the environment (Susam and Hudaverdi Ucer 2019. While ICT is obviously correlated with the production of equipment and the running of infrastructure (server parks and datacenters) (Røpke and Christensen 2012), it may also improve the reliability and efficiency of the transmission of the grid3This is in line with Laitner, John, and Martinez (2008) when argued that “for every kilowatt-hour consumed by ICT systems, a savings of 10 kilowatt-hours were enabled”., as well as enhance the storage and distribution of power (Amin and Rahman 2019). Through finer monitoring and grid control systems, expanding ICT may reduce the technology heterogeneity across countries, which is tightly linked to environmental performance (Amin and Rahman 2019; Houghton 2010; Liu, Chiu, and Liou 2017). Nonetheless, it must be highlighted that it also induces important amount of technological waste whose material recovery remains uncertain4According to Greenpeace (2006), the average lifespan of computers has decreased from six to two years over the period 1997–2005 in advanced economies. (Elliot 2007). According to the World Energy Council (WEC, 2018), ICT has been said to promote the reduction of Greenhouse Gas (GHG) emissions up to 125% by 2020. This is in line with the Earth Institute of Columbia University (2016) which listed several relevant ICT-based innovations in the energy sector making the Sustainable Development Goals (SDGs) more achievable (i.e., untitled “ICT & SDGs”). Among these innovations, Plant Information Models (PIM), Knowledge Organization Systems and Semantic Technology (KOSST), and Cyber Learning Platform for Network Education and Training (CLP4NET) are underlined. Thus, by offering the possibility of much faster technology upgrading, smart motor systems, as well as providing services at low cost, ICT systems are becoming a prominent energy-sector enabler.