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Geographic Feature Pipes Geographic Feature Pipes Marcell Roth Institute for Geoinformatics, University of Muenster, Germany [email protected] Abstract. Aggregating and combining data coming from different Web sources to create ad-hoc information refers to the concept of “piping” data. Linked Data is a solution which facilitates the browsing through related information and provides technologies to easily pipe data included in this Web of Data. The Open Geospatial Consortium (OGC) has estab- lished standards for the storage, retrieval, and processing of geospatial data. These standards act as foundation for the Spatial Data Infrastruc- tures. The integration of existing geospatial data into the Data Web is missing yet. The presented Geographic Feature Pipes (GFP) is an API deployed as free Web service working towards closing this gap. It trans- lates sensor data based on the OGC’s Observations and Measurements specification as well as geospatial data served by OGC Web Feature Ser- vices into its RDF representations. This enables complex queries and browsing through related geospatial data sources, as well as means of merging information of geographic features with related sensor data into one document. The translated data based on ontologies providing the vo- cabulary for the definition of the data entities. The presented approach shows that in conjunction with semantic annotations, we are able to bridge the gap between geospatial applications and Semantic Web tech- nologies to move toward the development of the Geospatial Semantic Web. 1 Introduction The Web is based on URLs as unique identifiers for documents and other data. These links allow users for browsing through the Web in order to retrieve infor- mation. Despite the advantages the Web offers, published data (information) is primary nested in HTML Web pages. HTML is about layouting content and not able to type links connecting an entity of the Web document to related entities [3]. Hyperlinks indicate that two documents are related, but leave it to the user to infer the nature of the relationship. Linked Data is a solution to create shared and structured information spaces [3], which include links between related infor- mation stating the nature of the connection. Its purpose is to create and connect related data on the Web with typed links, as if it would be one global database. To realize such a Web of Data, published data has to follow the Linked Data principles first outlined by Berners-Lee in 2006 [2]: the “raw” data is encoded in the machine-readable RDF [17], the data is Web addressable via URIs, and data is linked with other data via RDF links. RDF is a graph-based data model representing information with subject-predicate-object expressions (also called triples). A RDF link is one type of RDF triple and states that one data entity has some kind of relation to another data entity [4]. Linked Data promotes the reuse of information and reduces redundancy of existing information. It facil- itates the discovery of relevant information within the variety of information resources. Instead of following hyperlinks, users follow RDF links. The SPARQL Protocol and RDF Query Language (SPARQL) [25] supports users in formu- lating more sophisticated queries. Information of relationships stated in various RDF documents can be retrieved by querying across different sources. SPARQL also provides capabilities to easily combine information from different sources by merging two sets of triples into a single RDF graph [12]. Thus, new information can be created from the resulting dataset. Location is ubiquitous [11] and is an issue in many of the problems deci- sion makers must solve [16]. Such problems may vary from simple questions like “Where are my friends now?” to complex ones, for example, which areas are prone to floods in order to reduce the potential damage. Such geographic problems il- lustrate the increasing interest in geographic information (GI) in recent years. Geobrowsers like GoogleEarth1 or Microsoft’s Bing Maps2 are responses to user needs for location-based information services. They are part of the Geospatial Web [26], which makes GI shareable, searchable and ubiquitous for users and decision makers [8] by using the infrastructure of the Web. In the Geospatial Web, a distinction is made between geospatial data and services that facilitate the use of GI in many domain applications [14]. The variety of datasets con- taining GI reaches from simple map images to complex vector or sensor data. The Open Geospatial Consortium3 (OGC) developed the XML-based Geogra- phy Markup Language (GML) [24] as data modeling and encoding standard for GI, in particular when modeled as features, following the ISO/OGC reference model [23]. Vector data is conceived of as a feature, which is an abstraction of a real world phenomenon. Associated with a geographic location relative to the Earth, it is labeled as a geographic feature. Examples include buildings, streets, and rivers. Sensor data is stored and published using OGC’s Observations and Measurements (O&M) [6] model. Much of this data has been made available as Web services in the last decades. Web services are an important component in the fabric of the Geospatial Web [14], since they enable the sharing of geospatial data across organization boundaries over the Web [29]. Furthermore, they act on data and support discovery, retrieval and processing functionality. The OGC specifies implementation standards for such geospatial Web services. They are divided into various types: Web Feature Services (WFS) [28] serve vector-based data. A Sensor Observation Service (SOS) [22] provides a Web service interface to access observation results measured by sensors and sensor systems. Web Pro- cessing Services process or analyze geospatial data, e.g. the complex calculation of roadway noise. Various other OGC Web Services (OWS) exist and are listed 1 See http://earth.google.com/ 2 See http://www.bing.com/maps/ 3 See http://www.opengeospatial.org/ on the OGC Web site4. These services can be combined to Spatial Data Infras- tructures (SDI) to improve the interoperability between various data providers and users by smoothly exchanging and integrating GI. Despite the benefits the Geospatial Web provides, several open issues have to be discussed. GI is not well integrated in the Geospatial Web yet. It is possible to request GI from an OWS via an unique URL, e.g. a feature collection served by a WFS, but features (data entities) included in this dataset cannot be deref- erenced by people or clients. Consequently, links to information that is related to such a feature do not exist as well, although it would facilitate the Geographic Information Retrieval (GIR) [15]. Different OGC standards also raise compat- ibility issues across different applications. Merging different datasets, such as O&M and GML, into one dataset is very difficult. The transformation and publication of the OpenStreetMap [1] and Ordnance Survey [10] data according to the Linked Data principles have added a new dimension to the Web of Data. This work also adds spatial and temporal di- mensions to the Web of Data. With its benefits the work solves the mentioned issues of the Geospatial Web. In this paper we present our implementation of the Geographic Feature Pipes (GFP) which translate O&M based observations and GML features into RDF. This provides options to discover spatiotemporal data and possible related information by following RDF links or even to merge information related to a geographic feature into one document. GFP is a proxy- based solution [13] and a first step to bridge the gap between geospatial data included in the Geospatial Web and the Linked Data community. It increases the accessibility to non-OGC data sources. Features might be linked to Geon- ames5 entries. DBpedia6 entries might be connected with real-time sensor data. Providing features and observations as Linked Data make them accessible for a broad audience, which is maybe not aware of the geospatial Web services defined by the OGC. Linking them to entries included in the LinkedGeoData dataset [1] bridges the gap between the emerging Volunteered Geographic Information (bottom-up) data formats [9] and top-down standards like GML or O&M as well. Our approach adds extra knowledge to the datasets by using RDF-Schema (RDF-S) [5] ontologies for the definition of the geospatial linked data entities. Ontologies provide domain-specific terms for describing types of things in the real world and relations among those. Even more powerful queries can be for- mulated as a result. Linking information of the geographic domain to the Web of Data bridges the gap to the Semantic Web community as well. The remainder of this paper is structured as follows. A brief application sce- nario is introduced in Section 2 which illustrates the benefit of creating geospatial linked data entities. The implementation of GFP is described in Section 3, before we summarize and outline future work in Section 4. 4 See http://www.opengeospatial.org/standards 5 See http://www.geonames.org/ 6 See http://dbpedia.org/ 2 Application Scenario Creating GI following the Linked Data principles has several advantages. Geospa- tial linked data entities can easily be linked to related information and provide capabilities of merging two datasets to infer more information. It supports users to construct sophisticated queries and if the data is semantically annotated, the queries are even more powerful. The semantic enrichment of the underlying data models by linking them to formally specified vocabularies such as ontolo- gies is called semantic annotation [21,18]. Semantic query processing performed by reasoning engines like IRIS7 with semantic annotations return more precise discovery results. In the following we present an application scenario which il- lustrates the benefit of O&M based observations and GML features provided as linked data. Here we assume that the underlying data models, which are defined as RDF-S ontologies, are semantically annotated.
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