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Published in Claudia Lanza, Semantic Control for the Cybersecurity Domain, 2023
Managing technical terms proper to specialized languages represents one of the main tasks of Knowledge Organization Systems (KOSs). Simple Knowledge Organization System (SKOS), as stated by the W3C Recommendation of 20096 constitute: “[…] a common data model for sharing and linking knowledge organization systems via the Web.Many knowledge organization systems, such as thesauri, taxonomies, classification schemes and subject heading systems, share a similar structure, and are used in similar applications. SKOS captures much of this similarity and makes it explicit, to enable data and technology sharing across diverse applications.”
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.
Digital earth: yesterday, today, and tomorrow
Published in International Journal of Digital Earth, 2023
Alessandro Annoni, Stefano Nativi, Arzu Çöltekin, Cheryl Desha, Eugene Eremchenko, Caroline M. Gevaert, Gregory Giuliani, Min Chen, Luis Perez-Mora, Joseph Strobl, Stephanie Tumampos
In 1999, the Chinese Academy of Sciences responded by holding the first International Symposium on Digital Earth in Beijing, and in 2006, the International Society for Digital Earth (ISDE) was established1 Since then, the DE’s vision has been discussed and reviewed by several authors. Goodchild (1999) has outlined some of the research problems that arise from the original, Leclerc et al. (1999) presented number of problematic issues for navigating a large globe structure and proposed solutions to allow users to interact with DE efficiently and seamlessly. Grossner, Clarke, and Goodchild (2008), on the 10-year anniversary of Gore’s speech, reviewed DE from the perspective of a systematic software design process and found the envisioned system was in many respects inclusive of concepts of distributed geo-libraries and digital atlases. They offered and discussed a preliminary definition for a particular digital earth system as, ‘a comprehensive, distributed geographic information and knowledge organization system’ (Grossner, Clarke, and Goodchild 2008).
A recommender geoportal for geospatial resource discovery and recommendation
Published in Journal of Spatial Science, 2019
Shokouh Dareshiri, Mahdi Farnaghi, Mohammadreza Sahelgozin
Many studies utilised semantic web technology to improve the search mechanism of geoportals (Athanasis et al. 2009, Da Silva et al. 2009, Fugazza 2011, Iwanaik et al. 2011, Kalabokidis et al. 2011, Fugazza and Luraschi 2012, Gunay et al. 2014, Bogdanović et al. 2015). These studies mainly focused on issues related to multilingualism, semantic ambiguity and semantic heterogeneity (including synonymy, homonymy, etc.). They generally improved the search and discovery of geospatial resources using ontology and semantic web concepts, languages and technologies including RDF (Resource Description Framework), RDFS (RDF Schema), SKOS (Simple Knowledge Organization System), OWL (Web Ontology Language), etc. There are also studies that improved the accessibility of geospatial resources in geoportals using linked data concepts (Lacasta 2014, Vockner and Mittlböck 2014, Hu et al. 2015a, 2015b).
Modelling and publishing geographic data with model-driven and linked data approaches: case study of administrative units in Turkey
Published in Journal of Spatial Science, 2019
Arif Çağdaş Aydinoğlu, Abdullah Kara
In the last few years, none of the data modelling approaches has been popularized as much as ontologies that affect the computation science and database disciplines (Martinez-Cruz et al. 2012, 275). An ontology may provide for the sharing of a common understanding in specific domains, reusing domain information and expressing definitions, relations and interrelations of domains (Noy and McGuinness 2001). Ontologies define the classes, concepts, relations and rules and allow information to be understood by the people and the machines. They support interoperability, minimizing the problems concerning the integration of data coming from different systems (Gruber 1993). The World Wide Web Consortium (W3C) has developed ontology languages that present information at different definition levels on the Web: the Simple Knowledge Organization System (SKOS), the Resource Description Framework Schema (RDFS) and the Web Ontology Language (OWL). OWL should be used to develop comprehensive ontologies because it provides a representation of complex relationship definitions (e.g. disjoint, complementary), property characteristics (e.g. symmetric, transitive) and property constraints (e.g. choosing values from a single class and multiple classes). Furthermore, there are additional modelling possibilities of OWL such as owl:equivalentClass and owl:equivalentProperty axioms, which can be used to establish the equivalence connections for classes and for relations, respectively. When these axioms are combined with the rdfs:subClassOf and rdfs:subPropertyOf features, powerful mechanisms can be provided to define transformations between terms from different ontologies (Bizer and Heat 2011) and may enable interoperability between different data sets.