Patrick Maué

Semantic Enablement for Spatial Data Infrastructures

Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as a basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on-the-fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent Semantic Enablement Layer for SDI which also integrates reasoning services known from the Semantic Web. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world. Finally, we point out how to combine SDI with linked data.

Semantically-Enabled Sensor Plug & Play for the Sensor Web

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research.

Bottom-Up Gazetteers: Learning from the Implicit Semantics of Geotags

As directories of named places, gazetteers link the names to geographic footprints and place types. Most existing gazetteers are managed strictly top-down: entries can only be added or changed by the responsible toponymic authority. The covered vocabulary is therefore often limited to an administrative view on places, using only official place names. In this paper, we propose a bottom-up approach for gazetteer building based on geotagged photos harvested from the web. We discuss the building blocks of a geotag and how they relate to each other to formally define the notion of a geotag. Based on this formalization, we introduce an extraction process for gazetteer entries that captures the emergent semantics of collections of geotagged photos and provides a group-cognitive perspective on named places. Using an experimental setup based on clustering and filtering algorithms, we demonstrate how to identify place names and assign adequate geographic footprints. The results for three different place names (Soho , Camino de Santiago and Kilimanjaro ), representing different geographic feature types, are evaluated and compared to the results obtained from traditional gazetteers. Finally, we sketch how our approach can be combined with other (for example, linguistic) approaches and discuss how such a bottom-up gazetteer can complement existing gazetteers.

Data Integration in the Geospatial Semantic Web

Geospatial decision makers have to be aware of the varying interests of all stakeholders. One crucial task in the process is to identify relevant information available from the Web. In this chapter the authors introduce an application in the quarrying domain which integrates Semantic Web technologies to provide new ways to discover and reason about relevant information. The authors discuss the daily struggle of the domain experts to create decision-support maps helping to find suitable locations for opening up new quarries. After explaining how semantics can help these experts, they introduce the various components and the architecture of the software which has been developed in the European funded SWING project. In the last section, the different use cases illustrate how the implemented tools have been applied to real world scenarios.

SWING: an integrated environment for geospatial semantic web services

Geospatial Web services allow to access and to process Geospatial data. Despite significant standardisation efforts, severe heterogeneity and interoperability problems remain. The SWING environment leverages the Semantic Web Services (SWS) paradigm to address these problems. The environment supports the entire life-cycle of Geospatial SWS. To this end, it integrates a genuine end-user tool, a tool for developers of new Geospatial Web services, a commercial service Catalogue, the Web Service Execution Environment platform (WSMX), as well as an annotation tool. The demonstration includes three usage scenarios of increasing complexity, involving the semantic annotation of a legacy service, the semantic discovery of a Geospatial SWS, as well as the composition of a new Geospatial SWS.

Injecting Semantic Annotations into Geospatial Web service descriptions

Geospatial Web services comply with well-established standards to support seamless integration into applications ranging from commercial Geographic Information Systems GIS to open source web mapping clients. Descriptions of service capabilities contain information about the provided data. Updates to the underlying database result in changing descriptions. To ensure compatibility with existing solutions, semantic enablement of Geospatial Web services has to reflect both, standards and changing metadata. Semantic annotations link between legacy non-semantic Web service descriptions and their semantic counterparts. The open source Semantic Annotations Proxy SAPR is a light-weight RESTful API deployed as free service which “injects” semantic annotations into existing Web service descriptions without breaking the standards. This approach decouples the annotations from the original metadata, which ensures the separation of concerns between data providers and end users with different and sometimes conflicting views on annotations. In addition, the service is robust regarding changes of the service descriptions. The presented approach is focusing on W3C-and OGC-compliant Web services, but can be theoretically applied on any kind of information source with structured metadata.

Getting across information communities

Amazon’s deforestation affects citizens from varying information communities. Experts like scientists or journalists acquire relevant data on-site and publish valuable information on the Web. But different and sometimes conflicting views on reality impede sharing of information across communities, relevant content remains far too often undiscovered. We introduce rule-based semantic annotations as solution to facilitate the discovery and evaluation of geographical information. With the distinction between shared domain and local information source ontologies, the proposed architecture of a semantically supported SDI for the Amazon also takes the plethora of GI formats into account. Creating semantic annotations is challenging, a recommender system for semantic annotations enables even the non-IT experts to participate. The benefits of the proposed techniques are further illustrated by a scenario which spans across information communities of economics, ecology, and ethnology.

Semantic event processing in ENVISION

The Semantic Sensor Web provides a framework for the interoperable exchange and processing of observation data from heterogeneous sensor networks. Environmental monitoring applications deal with change detection in a spatio-temporal context. Here it is required to analyze vast amounts of sensor data in order to react to specific situations. In the ENVISION project, we explore how to enhance this process of sense&respond by using Complex Event Processing (CEP) techniques. CEP allows defining situations of change in observable variables using event patterns. By combining event processing and semantic technologies we are able to process time-series of observations from different sources and communicate the inferred events to interested communities in near real-time.

Semantic mediation on the Sensor Web

This work enables an automated integration of sensors and services on the Sensor Web. The Sensor Web is defined as an infrastructure which enables the interoperable usage of sensor resources by providing services for (1) discovery, (2) access, (3) tasking, as well as (4) eventing & alerting [2]. The notion of the Sensor Web has been largely influenced by the developments of OGCs Sensor Web Enablement (SWE) initiative [3], however, there are also other implementations complying to the Sensor Web idea, such as Sensorpedia [4], SensorMap with its underlying SenseWeb infrastructure [5], SensorBase [6], or Cosm2 (formerly known as Pachube).

The ENVISION Environmental Portal and Services Infrastructure

The ENVISION Portal is a Web-enabled infrastructure for the discovery, annotation, and composition of environmental services. It is a tool to create Web sites dedicated to particular domain-specific scenarios such as oil spill drift modeling or landslide risk assessment. The underlying architecture based on pluggable user interface components is briefly discussed, followed by a presentation of the components resulting from the first iteration of the implementation. A walkthrough explains how to create a scenario website and populate it with the user interface components required for one specific scenario. The paper concludes with a discussion of open challenges identified during the implementation.