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Building product models, terminologies, and object type libraries
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Aaron Costin, Jeffrey W. Ouellette, Jakob Beetz
Simple Knowledge Organisation System3 (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject heading systems, or any other type of structured controlled vocabulary. SKOS is an area of work-developing specifications and standards to support the use of knowledge organisation systems (KOS) such as thesauri, classification schemes, subject heading systems, and taxonomies within the framework of the semantic web. SKOS provides a standard way to represent knowledge organisation systems using RDF. Encoding this information in RDF allows it to be passed between computer applications in an interoperable way. Using RDF also allows KOSs to be used in distributed, decentralised metadata applications. Decentralised metadata is becoming a typical scenario, where service providers want to add value to metadata harvested from multiple sources.
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Published in Claudia Lanza, Semantic Control for the Cybersecurity Domain, 2023
The language SKOS systems use are RDF and OWL because of their exchangeable nature that make them readable and exploitable from several informative contexts. The model built by SKOS is based on the concept-scheme configuration where a range of concepts are identified by unambiguous URIs skos:Concept and are connected by a set of semantic associations and hierarchies. SKOS systems data model, by employing OWL language and RDF graph syntax, are able to merge the abstract configuration proper to ontology settings, which are based on axioms, with the thesaurus and classifications schemes outlines characterized by an organization of concepts through relationships associations. Indeed, each concept is defined by a label skos-label or alternative labels, that in most of the cases are synonyms expressed by RDF literals, then SKOS provide several descriptive labels, i.e., lexical or documentation properties [181]. In the context of KOSs, semantic resources, such as thesauri and ontologies, are useful tools to organize domain specific knowledge and to support processes like document indexing, information searching and retrieval and, in some cases, automatic reasoning (e.g., for decision making), above all in those specialized domains where semantic ambiguity between terms represent a step to be avoided.
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.
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.