ISO/DIS 19150-2:2014 - Geographic information -- Ontology -- Part 2: Rules for developing ontologies in the Web Ontology Language (OWL)
... The availability of standardized geospatial information on the Semantic Web is still limited, but some effort has been put into the issue by OGC and W3C, who established The Spatial Data on the Web Working Group in cooperation in 2015 [34]. ISO/TC 211 developed the standard ISO 19150, part 2 [35] with rules for conversion from UML to OWL. The ISO/TC 211 Group for Ontology Management (GOM) [36] derived OWL ontologies for most ISO/TC 211 standards following the rules from ISO 19150-2. ...
... The state-of-the-art study of conversions from geospatial UML models to OWL ontologies presented in [1] was extended for this article with work described in additional sources and more detailed analysis of conversion rules. The main source of the studies is the conversion rules defined in ISO 19150, part 2 [35]. The AR3NA project refined and modified the rules from ISO 19150-2 into guidelines for RDF encoding of geospatial information and models defined according to the INSPIRE Directive in Europe [37]. ...
... Package Ontology Name and structure as in UML [35]. Name and structure from tagged values [37,38]. ...
This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.
... In each, application schemas that are domain-specific (e.g. for geology) are distinguished from standard schemas that are relatively domain-neutral (e.g. for geometry), with the key difference being that standard schemas are applicable to many domains. Both kinds of schema can be expressed in a conceptual modelling language (CML) such as UML or OWL [22,24], or a data language (DL) such as GML-XML [32]; additional rules are also established for conversion amongst them [24,32]. A schema following OGC/ISO protocols and expressed in a CML is referred to as a conceptual schema by OGC/ISO, but to avoid confusion we henceforth refer to such schemas as CML schemas, and use conceptual schema for the more general data modelling notion. ...
... In each, application schemas that are domain-specific (e.g. for geology) are distinguished from standard schemas that are relatively domain-neutral (e.g. for geometry), with the key difference being that standard schemas are applicable to many domains. Both kinds of schema can be expressed in a conceptual modelling language (CML) such as UML or OWL [22,24], or a data language (DL) such as GML-XML [32]; additional rules are also established for conversion amongst them [24,32]. A schema following OGC/ISO protocols and expressed in a CML is referred to as a conceptual schema by OGC/ISO, but to avoid confusion we henceforth refer to such schemas as CML schemas, and use conceptual schema for the more general data modelling notion. ...
The explosive growth of geospatial data has stimulated the development of many standards aimed at decreasing data heterogeneity and enhancing data use. Well-established design methods for geospatial data standards typically involve the creation of two schemas for data structure, designated here as logical and physical, but this can lead to conceptual inconsistencies and modelling inefficiencies. In this paper we describe a design method that overcomes these issues by incorporating an additional schema – the conceptual schema – and demonstrate its application to the design of GroundWaterML2 (GWML2), a new international standard for groundwater data. Results include not only a new data standard, robustly constructed and tested, but also an enhanced method for geospatial data standard design.
... However, (a) OGC O&M is defined using UML and (b) some of the class and property names have been adjusted. Therefore, a formal (axiomatized) alignment to OGC O&M is provided as a vertical module which owl:imports SOSA, and uses the URI scheme defined by ISO 19150-2 to denote the O&M classes and properties [27]. The main conceptual difference between SSN and O&M is that the latter conflates both Procedures and Systems into a pair of classes: OM_Process and SF_Process. ...
... However, (a) OGC O&M is defined using UML and (b) some of the class and property names have been adjusted. Therefore, a formal (axiomatized) alignment to OGC O&M is provided as a vertical mod- ule which owl:imports SOSA, and uses the URI scheme defined by ISO 19150-2 to denote the O&M classes and properties [27]. ...
The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies. A. Haller et al. / The Modular SSN Ontology: A Joint W3C and OGC Standard 1
... However, (a) OGC O&M is defined using UML and (b) some of the class and property names have been adjusted. Therefore, a formal (axiomatized) alignment to OGC O&M is provided as a vertical mod- ule which owl:imports SOSA, and uses the URI scheme defined by ISO 19150-2 to denote the O&M classes and properties [27]. ...
The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies. A. Haller et al. / The Modular SSN Ontology: A Joint W3C and OGC Standard 1
... The standard on methodology for feature cataloguing, ISO 19110 [10], described how to develop geographic information feature catalogue based on the GFM for the documentation semantics of geographic information in support to an application schema. More recently, ISO/TC 211 has addressed the development of ontology within a framework for ontology development, ISO19150-1 [11], and rules for developing ontologies in the Web Ontology Language (OWL), ISO19150-2 [12], to support the semantics of geographic information as part of the Geospatial Semantic Web. Finally, ISO/TC 211 is in the process to provide its harmonized model in OWL, the ISO/TC 211 harmonized ontology. ...
... Proposed ontology model has been verified on several examples, including integration with international frameworks such as INSPIRE, integration of spatial and non-spatial data, semantic search based on reasoning to find party portfolio, and distributed queries in SPARQL to retrieve data. Future work will include the alignment with the emerging standard for geospatial ontologies-ISO 19150 [21,22] that defines the framework for semantic interoperability of geographic information and rules for geospatial ontologies, where one of the parts of this standard should address semantic operators and service ontology, once the work on it starts. Therefore, our further research will be focused on semantic integration and composition of cadastral Web services, aligned with standards. ...
This paper presents the application of ontologies in the field of real estate cadastre. Ontologies can be seen as a form of metadata that provide a higher level of interoperability and integration within the Spatial Data Infrastructure, not only on the syntax level but on the semantic level as well. The application of ontologies in this domain is based on domain ontology for cadastre developed on top of the Land Administration Domain Model defined in ISO 19152 standard. The use of ontologies is shown on the several examples including data integration of the Serbian national cadastre and the INSPIRE cadastral parcels, and integration of OGC based geospatial services and other Web services in cadastral systems. The introduction of semantics in the cadastral systems provide many opportunities in terms of cadastral data and services integration on national and international level, and also in connecting with many other organizations that are users of such systems. These opportunities are reflected in the fact that terms are given well-defined explicit meaning and when based on formal ontology automatic reasoning can be used to infer the new knowledge.
This article has been corrected. Link to the correction 10.2298/CSIS151230002E
... ISO/TC 211 geographic information standards and INSPIRE are using UML schema as object oriented semantics in description of the data model . ISO/TC211 geoinformation standardization of ontology approach is intended to provide General Feature Model (GFM) modular models that can be joined by ontologies (ISO 2010a and2010b). Ontology is giving possibility to connect different views of the real or hypothetical world that includes everything of interest; e.g. universe of discourse. ...
In the past toponyms were the main tool to make spatial relations and spatial reasoning. They are reflecting spatial relations in historical, cultural and other contexts. Toponyms as base of knowledge about spatial relations contain a lot of information. After theory of multiple intelligences proposed by Howard Gardner in 1983, spatial intelligence is one of the more recognized intelligences (bodily-kinesthetic, linguistic, logical-mathematical, musical, interpersonal, intrapersonal, naturalistic and spatial). The most common description of spatial intelligence is the ability to be able to recreate one's visual experience and reasoning about shape, measurement and orientation. In the conceptualization of the spatial relations using toponyms spatial intelligence is not based only on the visual experience. Spatial reasoning using toponyms is the ability to reason about spatial relations using toponyms as objects that are containing spatial information. It is the ability to extract position and orientation from toponym in everyday life. Toponyms are often not taken as the complete object that is built of information about geographical feature, noun and position in the space. Without any of the element toponym is not completely defined. For example, spatial reasoning using not completely defined toponym can lead to cardinal mistakes; noun Berlin can be connected to capital of Germany, but also settlement in Finland and region in France. Spatial intelligence is mostly connected to visual sense and visualization of space features. Because of that is sometimes term visual thinking used. But, blind persons also have spatial intelligence and spatial intelligence is not exclusively tied to the visual sense. Toponyms can be given in narrative way and not only in the written form. They are also not primarily connected to visual effects. Standardization of toponyms and development of Spatial Data Infrastructure (SDI) or spatial Information and Communication Technologies (ICT) systems is using toponyms in semantic approach that is leading to conceptual models. Ontology, as new trend is still not fully standardized. ISO is writing more standards on ontology to giving more possibilities in connecting different bases of knowledge and that is important for toponyms whose elements are defined in different disciplines. Definitions of toponyms made by United Nations Group of Experts on Toponyms (UNGEGN) and EU INSPIRE have small differences that are defining different base of knowledge and can lead to different developments and practical solutions. That can lead to different spatial reasoning using toponyms. This work is indicating basic relations between toponyms and the spatial intelligence and spatial reasoning.
Intelligent Knowledge as a Service (iKaaS) is an ambitious project aiming at integrating sensor management using Internet of Things (IoT) and cloud services by exploiting sensor data. The platform design covers self-healing function based on self-awareness as well as basic functions such as inter-cloud, security/privacy management, and devices and data management.
From the viewpoint of application development, ontology sharing is the most important to integrate services. This paper, as the first step towards ontology sharing, defines the iKaaS data model which integrates data models used in all the applications in the project. The data defined in iKaaS data model is converted into RDF format, stored in RDF database. The reasoning mechanism in semantic web allows the semantic integration of data and applications.
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Linked data provide an alternative route for the dissemination of spatial information compared to the traditional SOA-based SDI approach. The traditional approach has provided a wealth of standardized and structured location data based on Geography Markup Language (GML), while linked data provides an open mechanism for sharing and combining this data with anything, once the data is available as linked data. The first part of the paper focuses on deriving linked data from GML data. In the second part, we study how more meaningful data, expressed in Resource Description Framework (RDF) can be created from GML, given the underlying information model, by transforming it from Unified Modeling Language (UML) to Web Ontology Language (OWL).
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