It is still a problem for current information systems and modeling methods to make heterogeneous data sources interoperable and to link data sets obtained from a common territory by different experts (biologists, ecologists, geographers, hydrologists etc.) Existing observation systems are complex and can record either a long term observation at the large scale, or short term validity data at, for example, the molecular or cellular level or territorial and regional level. The objective of this project is to develop an infrastructure to allow the different available observation data sets to be brought into a coordinated platform able to address questions relating to different correlated impacts from the natural environment, land use or climate evolution. The challenge concerns multidisciplinary interoperability and modeling interdependent observation systems, across different space scales and time granularity. Our approach requires developing a multidimensional spatio-temporal model (4D + spatial scale + time granularity) based on a semantic linked data architecture.

The H2020 Mineral Intelligence Capacity Analysis (MICA) project is a collaboration between multiple geological surveys (BRGM, BGS, GEUS, GeoZS) in Europe; the European Raw Materials Intelligence Capacity Platform (EU-RMICP) enables the user to find information about mineral raw material in Europe. This paper discusses a part of EU-RMICP platform based on the models and the Web of data architecture. This part of the platform focusing on mineral resources, methods and information collected in different European data-bases, to leverage semantic technologies and to manage and link geoscience information and resources. Experts deal with many representations by taking into account the environmental, technical, political and social dimensions, metadata, heterogeneous data sources and tools. Our solution for semantic interoperability between different resources, is based on semantic annotation by adding knowledge to resources with semantic tags. The semantics attached to resources is defined by ontology and the exploitation is made with an integrated model, which maps the correspondence between the ontology and the resources. The ontology is based on an ontology of the domain of mineral resources coupled with relative commodities, time and space. The study focuses on the topic of semantic modeling, exploratory data, ranking the queries results and the query of linked data are introduced through Euro-Lex datasets. The architecture is based on a RDF triple store storing the ontology, the methods and documentation, the scenarios and metadata. The triple store is connected with eight existing European Data bases, and with the inference engine to search, select, infer and rank the results.

These proceedings contain the papers selected for presentation at the sixth edition of the International Symposium on Web & Wireless Geographical Information Systems held in Hong Kong during December

Résumé. The Coordinated Online Information Network (COIN) incorporates semantic web technology that integrates, publishes and visualizes time series water data allowing users to access a multitude of datasets in order to compare the data and draw conclusions. It employs an efficient and systematic data storage and retrieval system and can convert the data to a standard format for visualization. COIN utilizes a number of standards from OGC (Open Geospatial Consortium) and W3C (RDF, OWL, SPARQL and GeoSPARL) and benefits from generic ontologies transforming semantics to data, enriching data sets and making it available and interoperable via WFS and WMS standards. These principles facilitate publication and exchange of data across the web, increasing transparency and interpretability. Through modernized data submission and retrieval we hope to break down the silos of data, allowing users to visualize time series water quality and hydrometric data from multiple sources to increase knowledge in relationship to impacts on Yukon water.

The Coordinated Online Information Network (COIN) is a spatial data infrastructure (SDI) which provides an online network of resources to share, use and integrate information of geographic locations in North Canada. COIN incorporates semantic web technology that integrates, publishes and visualizes time series water data allowing users to access a multitude of datasets in order to compare the data and draw conclusions. COIN utilizes a number of standards from OGC (Open Geospatial Consortium) and W3C (Resource Description Framework, RDF, Web Ontology Language OWL, SPARQL query language for RDF) and GeoSPARQL for geospatial query). COIN benefits from generic ontologies transforming data into semantics, enriching data sets and making the data available and interoperable via WFS and WMS standards. These principles facilitate publication and exchange of data across the web, increasing transparency and interpretability. Through modernized data submission and retrieval we hope to break down the silos of data, allowing users to visualize time series water quality and hydrometric data from multiple sources to increase knowledge in relationship to impacts on Yukon water.

This work, stemming from a collaboration between t he departments of biology of the University of Queen's, the Canadian Circumpolar I nstitute (CCI) of Alberta University and the Laboratory of Computing of Grenoble (LIG), is dedicated to the implementation of a platform for publishing and analyzing environmental data by using semantic Web standards. The data concern the chemistry and the biology of t he water in Arctic and subarctic regions. These data sets are structured in a series of measu rements made during summer sampling collections. This platform is a first prototype which demonstrate s the interest in publishing and exploiting the data using semantic Web standards. We shall dem onstrate how initial tabular data can be represented by an RDF graph based on vocabularies ato domain ontologies which allow the data a richer semantics; how these data can be linked to other data available on the Web; and how these enriched data then can be queried , via a cartographic interface, through requests which exploit their semantics.

The Linked Data initiative has made it possible for the web to evolve from being a global information space in which only documents are linked to one in which both documents and data are linked: a web of documents and data. This tutorial aims to give an overview of the principles, models and technologies underlying Linked Data.

The Linked Open Data (LOD) cloud contains vast spatio-temporal information that can be exploited by the location-based mobile applications and presented using Augmented Reality (AR). While AR shows to be well suited for searching and browsing location-based information, most approaches focus on domain specific scenarios and there is no generic data model for the information search and discovery that could be re-used in various applications. The geo-referenced data, however, is already available on the LOD cloud and can be exploited to provide location-based information to the user. With this approach, in this paper, we present a generic architecture for the surroundings discovery mobile applications. The 3D models that we publish on the LOD cloud represent the real world objects that belong to different temporal intervals. These geometric models allow the user to mentally construct the referential relationship between virtual and real-world objects and the mobile applciation developer to create experiences based on different concepts on the cloud. This way, the LOD cloud, which is a growing structured source of semantic data, becomes the main source of information for the architecture, and facilitates the knowledge discovery. We also extend our mobile location-based application, ARCAMA-3D ( Augmented Reality for Context Aware Mobile Applications with 3D ) that we have developed previously, by using this architecture.

ARCAMA-3D (Augmented Reality for Context Aware Mobile Applications with 3D) is a mobile platform that allows us to overlay a 3D representation of the surroundings with augmented reality objects. In this paper, we show how these 3D objects, which are overlaid on the real view captured by the camera of the mobile device, are coupled with the Linked Open Data (LOD) cloud. With this approach, the data aggregation for a mobile augmented reality system is provided using the interconnected knowledge bases on the Web. This offers the possibility to enrich our 3D objects with structured information on the cloud. The objects that act as an augmented reality interface are used to provide an interactive access to these information. This approach provides an opportunity for people who publish information on the LOD cloud to interlink their data with 3D urban models. In order to achieve this, we propose an extensible data model that takes into account the temporal evolution of real world entities (such as buildings, monuments, etc.) and we publish our 3D models using this data model.