Applying Two-Level Modelling to Remote Sensor Systems Design to Enable Future Knowledge Generation

Geographical Information Scientists have a need to combine data from many sources and in various ways to synthesize new understanding, producing new knowle­dge [1]. Remote sensor deployments, monitoring environmental phenomena, are a huge provider of valuable data. Often, observation systems are built in isolation, and the data representations are not adequately designed for re-use and higher order knowledge generation. There are many standards that allow syntactic interoperability and sharing of remote sensor systems observational data, such as the OGC’s suite of standards [2]. However, semantic interoperability remains a work in progress [3] [4]. This presentation describes how system design techniques used in the health informatics domain [5] to tackle similar problems of how data, information and knowledge concepts are modelled and managed can be applied to remote sensing applications. Much like the health domain, remotely sensed data is traditionally modelled from a computer science perspective. Traditional object-oriented techniques typically used to model complex data are insufficient in a geographical data context, as they are too stringent during the early stages of knowledge acquisition. Standards such as O&M on their own precipitate a codifying effect as systems are developed, constraining rapidly evolving information [6]. The authors have investigated the OGC’s O&M standard as a reference model to underpin a two-level modelling approach. An augmented O&M model has been developed and is presented along with a worked example of how a two-level modelling approach using O&M as the reference model can be applied to modelling a marine data buoy. [1] M. Gahegan and W. Pike, A situated knowledge representation of geographical information, Transactions in GIS, vol. 10, pp. 727-749, 2006. [2] M. Botts, G. Percivall, C. Reed and J. Davidson, OGC® sensor web enablement: Overview and high level architecture, in GeoSensor Networks Springer, 2008, pp. 175-190. [3] S. Cox, An explicit OWL representation of ISO/OGC observations and measurements. in Ssn@ Iswc, 2013, pp. 1-18. [4] A. M. Leadbetter, R. K. Lowry and D. O. Clements, Putting meaning into NETMAR–the open service network for marine environmental data, International Journal of Digital Earth, pp. 1-18, 2013. [5] T. Beale, Archetypes: Constraint-based domain models for future-proof information systems, in OOPSLA 2002 Workshop on Behavioural Semantics, 2002. [6] M. F. Goodchild, GIScience ten years after Ground Truth, Transactions in GIS, vol. 10, pp. 687-692, 2006

A Harmonized Vocabulary For Water Quality

Interoperability of water quality data depends on the use of common models, schemas and vocabularies. However, terms are usually collected during different activities and projects in isolation of one another, resulting in vocabularies that have the same scope being represented with different terms, using different formats and formalisms, and published in various access methods. Significantly, most water quality vocabularies conflate multiple concepts in a single term, e.g. quantity kind, units of measure, substance or taxon, medium and procedure. This bundles information associated with separate elements from the OGC Observations and Measurements (O&M) model into a single slot. We have developed a water quality vocabulary, formalized using RDF, and published as Linked Data. The terms were extracted from existing water quality vocabularies. The observable property model is inspired by O&M but aligned with existing ontologies. The core is an OWL ontology that extends the QUDT ontology for Unit and QuantityKind definitions. We add classes to generalize the QuantityKind model, and properties for explicit description of the conflated concepts. The key elements are defined to be sub-classes or sub-properties of SKOS elements, which enables a SKOS view to be published through standard vocabulary APIs, alongside the full view. QUDT terms are re-used where possible, supplemented with additional Unit and QuantityKind entries required for water quality. Along with items from separate vocabularies developed for objects, media, and procedures, these are linked into definitions in the actual observable property vocabulary. Definitions of objects related to chemical substances are linked to items from the Chemical Entities of Biological Interest (ChEBI) ontology. Mappings to other vocabularies, such as DBPedia, are in separately maintained files. By formalizing the model for observable properties, and clearly labelling the separate concerns, water quality observations from different sources may be more easily merged and also transformed to O&M for cross-domain applications

Standardized Information Models to Optimize Exchange, Reusability and Comparability of Citizen Science Data. A Specialization Approach

The number of citizen science projects is constantly growing. Local, national, and international platforms feature new projects almost every month, resulting in an endless number of new observations that are constantly gathered and stored in databases. Often, these data sets are only used for the sampling campaign’s objectives, thus leaving a huge potential unused: its reusability in other contexts and its comparability with other data sets. Reusability and comparability require a number of aspects to be fulfilled. This paper describes those aspects and focuses on the citizen science application profile as a standardized information model to ensure syntactic and semantic understanding of citizen science data. Data compliant with this information model can be discovered and accessed through standardized Web interfaces and therefore easily integrated into any data processing environment or compared to any other data set. It is emphasized that the application profile described in this paper is one of two possible solutions that are currently being explored. The second one is briefly addressed and will be documented in detail in future publications

Semantic linking of complex properties, monitoring processes and facilities in web-based representations of the environment

Where a virtual representation of the Earth must contain data values observed within the physical Earth system, data models are required that allow the integration of data across the silos of various Earth and environmental sciences domains. Creating a mapping between the well-defined terminologies of these silos is a stubborn problem. This paper presents a generalised ontology for use within Web 3.0 services, which builds on European Commission spatial data infrastructure models. The presented ontology acknowledges that there are many complexities to the description of environmental properties which can be observed within the physical Earth system. The ontology is shown to be flexible and robust enough to describe concepts drawn from a range of Earth science disciplines, including ecology, geochemistry, hydrology and oceanography. This paper also demonstrates the alignment and compatibility of the ontology with existing systems and shows applications in which the ontology may be deployed

IMPROVEMENTS IN AUTOMATED DERIVATION OF OWL ONTOLOGIES FROM GEOSPATIAL UML MODELS

Standards from ISO/TC 211 are the foundation for modelling a universe of discourse in a geospatial context. UML models based on the standards, and in particular based on the UML profile defined in ISO 19103, have been developed and implemented in applications and databases for a wide range of geospatial information, from international to national and agency level. Amounts of information has been collected, maintained and made available based on the models, but mainly through specific services and exchange formats for geospatial information. To make the models and the information available in The Semantic Web, the geospatial UML models need to be transformed from UML to OWL ontologies, and the information needs to be transformed from UML-based structures to RDF triples. This paper investigates methods for transforming UML models of geospatial information to OWL ontologies, identifies challenges, suggest improvements and identifies needs for further research. Several methods for automated transformation from geospatial UML models to OWL handle basic concepts, but some concepts and context-closed restrictions from UML cannot be directly transformed to the open world of The Semantic Web. None of the analysed methods handles all of these issues, and suggested improvements include combining and improving transformation rules, as well as modifications in the UML models. To what degree and how these issues need to be handled will depend on whether the scope of the ontologies is to simply present geospatial information on The Semantic Web, or if they shall be used in a bidirectional information exchange

Generación y publicación de Linked Data para el monitoreo de la calidad ambiental del agua

El agua dulce resulta un recurso de vital importancia para la salud humana, la sostenibilidad del medio ambiente y la prosperidad económica. Por tanto, hace necesario el desarrollo de sistemas de información que permitan el monitoreo de este recurso, teniendo en cuenta aspectos como cantidad y calidad. Para facilitar su monitoreo se propone adoptar Linked Data, que consiste en un conjunto de buenas prácticas que no solo buscan la publicación de información estructurada en la Web, sino que también apuesta por la interconexión entre distintas fuentes de datos, estandarización e interoperabilidad. La adopción de estas buenas prácticas (Linked Data), también involucran el uso de vocabularios (ontologías), para permitir dar significado a la información contenida y que ésta se convierta en conocimiento una vez se ha puesto en contexto. De igual manera, la adopción de estas buenas prácticas brinda soporte para la interoperabilidad, especialmente semántica, de los sistemas con información geoespacial de tipo ambiental (contaminación hídrica). Este trabajo propone una solución que permita poner dentro del contexto legal vigente información correspondiente a características físico-químicas y microbiológicas de cuerpos hídricos y, al mismo tiempo, facilitar la conexión de estos datos con otras fuentes de información mediante los principios de Linked Data. Asimismo, esta propuesta busca promover el aprovechamiento de ontologías existentes e, incluso, de recursos no ontológicos, conforme a las recomendaciones presentes en el estado del arte, para llevar a cabo la definición del tipo de agua, datos y usos asociados, lo que conducirá al desarrollo de un caso de estudio para la interpretación eficiente de los datos hídricos de la cuenca del río Bogotá, indicando la peligrosidad y el potencial del uso del agua de acuerdo a la legislación que le aplique en el contexto requerido y su uso.Abstract: Fresh water is a resource of vital importance for human health, the sustainability of the environment and economic prosperity. Therefore, it is necessary to develop information systems that allow the monitoring of this resource, taking into account aspects such as quantity and quality. In order to facilitate its monitoring, we propose to adopt Linked Data, which consists of a set of good practices that not only seek the publication of structured information on the Web, but also achieves interconnection between different data sources, standardization and interoperability. The adoption of these good practices (Linked Data), also involves the use of vocabularies (ontologies), to allow giving meaning to the contained information and that this becomes knowledge once it has been put into context. In the same way, the adoption of these good practices provides support for the interoperability, especially semantics, of the systems with geospatial information of environmental type (water pollution). This work proposes a solution that allows putting within the current legal context information corresponding to physical-chemical and microbiological characteristics of water bodies and, at the same time, facilitate the connection of these data with other sources of information through the guidelines of Linked Data. In addition, this proposal seeks to promote the use of existing ontologies and even non-ontological resources, in accordance with the recommendations present in the state of the art, to carry out the definition of the type of water, data and associated uses. This scenario is developed in a case study for the efficient interpretation of water data of the Bogotá river basin, indicating the danger and potential of water use according to the regulation that applies to it in the required context and its use.Maestrí

Applying Two-Level Modelling to Remote Sensor Systems Design to Enable Future Knowledge Generation

Geographical Information Scientists have a need to combine data from many sources and in various ways to synthesize new understanding, producing new knowle­dge [1]. Remote sensor deployments, monitoring environmental phenomena, are a huge provider of valuable data. Often, observation systems are built in isolation, and the data representations are not adequately designed for re-use and higher order knowledge generation. There are many standards that allow syntactic interoperability and sharing of remote sensor systems observational data, such as the OGC’s suite of standards [2]. However, semantic interoperability remains a work in progress [3] [4]. This presentation describes how system design techniques used in the health informatics domain [5] to tackle similar problems of how data, information and knowledge concepts are modelled and managed can be applied to remote sensing applications. Much like the health domain, remotely sensed data is traditionally modelled from a computer science perspective. Traditional object-oriented techniques typically used to model complex data are insufficient in a geographical data context, as they are too stringent during the early stages of knowledge acquisition. Standards such as O&M on their own precipitate a codifying effect as systems are developed, constraining rapidly evolving information [6]. The authors have investigated the OGC’s O&M standard as a reference model to underpin a two-level modelling approach. An augmented O&M model has been developed and is presented along with a worked example of how a two-level modelling approach using O&M as the reference model can be applied to modelling a marine data buoy. [1] M. Gahegan and W. Pike, "A situated knowledge representation of geographical information," Transactions in GIS, vol. 10, pp. 727-749, 2006. [2] M. Botts, G. Percivall, C. Reed and J. Davidson, "OGC® sensor web enablement: Overview and high level architecture," in GeoSensor Networks Springer, 2008, pp. 175-190. [3] S. Cox, "An explicit OWL representation of ISO/OGC observations and measurements." in Ssn@ Iswc, 2013, pp. 1-18. [4] A. M. Leadbetter, R. K. Lowry and D. O. Clements, "Putting meaning into NETMAR–the open service network for marine environmental data," International Journal of Digital Earth, pp. 1-18, 2013. [5] T. Beale, "Archetypes: Constraint-based domain models for future-proof information systems," in OOPSLA 2002 Workshop on Behavioural Semantics, 2002. [6] M. F. Goodchild, "GIScience ten years after Ground Truth," Transactions in GIS, vol. 10, pp. 687-692, 2006