Carsten Keßler

The use of Web 2.0 concepts to support deliberation in spatial decision-making

Technologies associated with the second-generation of the World-Wide Web enable virtually anyone to share their data, documents, observations, and opinions on the Internet. In less than three years, mapping platforms such as Google Maps have sparked an exponential growth in user-generated geographically referenced content. However, the “serious” applications of Web 2.0 are sparse and this paper assesses its use in the context of collaborative spatial decision-making. We present an online map-based discussion forum that enables Internet users to submit place-based comments and respond to contributions from other participants. We further use the geographic references in a thread-based master plan debate for a university campus to simulate this debate in the map-based forum. This allows us to demonstrate how the online map provides an overview of the status and spatial foci of the debate, and how it can help us understand the spatial thought processes of the participants.

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.

An agenda for the next generation gazetteer: geographic information contribution and retrieval

Gazetteers are key components of georeferenced information systems, including applications such as Web-based mapping services. Existing gazetteers lack the capabilities to fully integrate user-contributed and vernacular geographic information, as well as to support complex queries. To address these issues, a next generation gazetteer should leverage formal semantics, harvesting of implicit geographic information -- such as geotagged photos -- as well as models of trust for contributors. In this paper, we discuss these requirements in detail. We elucidate how existing standards can be integrated to realize a gazetteer infrastructure allowing for bottom-up contribution as well as information exchange between different gazetteers. We show how to ensure the quality of user-contributed information and demonstrate how to improve querying and navigation using semantics-based information retrieval.

Algorithm, implementation and application of the SIM-DL similarity server

Semantic similarity measurement gained attention as a methodology for ontology-based information retrieval within GIScience over the last years. Several theories explain how to determine the similarity between entities, concepts or spatial scenes, while concrete implementations and applications are still missing. In addition, most existing similarity theories use their own representation language while the majority of geo-ontologies is annotated using the Web Ontology Language (OWL). This paper presents a context and blocking aware semantic similarity theory for the description logic ALCHQ as well as its prototypical implementation within the open source SIM-DL similarity server. An application scenario is introduced showing how the Alexandria Digital Library Gazetteer can benefit from similarity in terms of improved search and annotation capabilities. Directions for further work are discussed.

Trust as a Proxy Measure for the Quality of Volunteered Geographic Information in the Case of OpenStreetMap

High availability and diversity make Volunteered Geographic Information (VGI) an interesting source of information for an increasing number of use cases. Varying quality, however, is a concern often raised when it comes to using VGI in professional applications. Recent research directs towards the estimation of VGI quality through the notion of trust as a proxy measure. In this chapter, we investigate which indicators influence trust, focusing on inherent properties that do not require any comparison with a ground truth dataset. The indicators are tested on a sample dataset extracted from OpenStreetMap. High numbers of contributors, versions and confirmations are considered as positive indicators, while corrections and revisions are treated as indicators that have a negative influence on the development of feature trustworthiness. In order to evaluate the trust measure, its results have been compared to the results of a quality measure obtained from a field survey. The quality measure is based on thematic accuracy, topological consistency, and information completeness. To address information completeness as a criterion of data quality, the importance of individual tags for a given feature type was determined based on a method adopted from information retrieval. The results of the comparison between trust assessments and quality measure show significant support for the hypothesis that feature-level VGI data quality can be assessed using a trust model based on data provenance.

Semantic rules for context-aware geographical information retrieval

Geographical information retrieval (GIR) can benefit from context information to adapt the results to a users current situation and personal preferences. In this respect, semantics-based GIR is especially challenging because context information - such as collected from sensors - is often provided through numeric values, which need to be mapped to ontological representations based on nominal symbols. The Web Ontology Language (OWL) lacks mathematical processing capabilities that require free variables, so that even basic comparisons and distance calculations are not possible. Therefore, the context information cannot be interpreted with respect to the task and the current users preferences. In this paper, we introduce an approach based on semantic rules that adds these processing capabilities to OWL ontologies. The task of recommending personalized surf spots based on user location and preferences serves as a case study to evaluate the capabilities of semantic rules for context-aware geographical information retrieval. We demonstrate how the Semantic Web Rule Language (SWRL) can be utilized to model user preferences and how execution of the rules successfully retrieves surf spots that match these preferences. While SWRL itself enables free variables, mathematical functions are added via built-ins - external libraries that are dynamically loaded during rule execution. Utilizing the same mechanism, we demonstrate how SWRL built-ins can query the Semantic Sensor Web to enable the consideration of real-time measurements and thus make geographical information retrieval truly context-aware.

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.

Similarity measurement in context

Context plays a crucial role when measuring the similarity of two concepts. Nonetheless, the modelling of context has been mostly neglected in existing similarity measurement theories. In this paper, we explore the influence of context in existing similarity measurement approaches for the geospatial domain, focussing on whether and how these approaches account for it. Based on these observations, the processing of context during similarity measurement is analysed, and general implementation issues, especially ease of integration into existing reasoning systems and computability, are discussed. The results of the different analyses are then combined into a generic set of characteristics of context for similarity measurement, with regard to the geospatial domain.

The effect of context on semantic similarity measurement

Similarity measurement is currently being established as a method to explore content on the Semantic Web. Semantically annotated content requires formal concept specifications. Such concepts are dynamic and their semantics can change depending on the current context. The influence of context on similarity measurement is beyond dispute and reflected in recent similarity theories. However, the systematics of this influence has not been investigated so far. Intuitively, the results of similarity measurements should change depending on the impact of the current context. Particularly, such change should converge to 0 with a decreasing impact of the respective contexts. To hold up to this assertion, a quantification of the impact of context on similarity measurements is required. In this paper, we use a combination of the SIM-DL theory, which measures similarity between concepts represented using description logic, and a context model distinguishing between internal and external context to quantify this impact. The behavior of similarity measurements within an ontology specifying geospatial feature types is observed under varying contexts. The results are discussed with respect to the corresponding impact values.

What is the difference? A cognitive dissimilarity measure for information retrieval result sets

Result rankings from context-aware information retrieval are inherently dynamic, as the same query can lead to significantly different outcomes in different contexts. For example, the search term Digital Camera will lead to different—albeit potentially overlapping—results in the contexts customer reviews and shops, respectively. The comparison of such result rankings can provide useful insights into the effects of context changes on the information retrieval results. In particular, the impact of single aspects of the context in complex applications can be analyzed to identify the most (and least) influential context parameters. While a multitude of methods exists for assessing the relevance of a result ranking with respect to a given query, the question how different two result rankings are from a user’s point of view has not been tackled so far. This paper introduces DIR, a cognitively plausible dissimilarity measure for information retrieval result sets that is based solely on the results and thus applicable independently of the retrieval method. Unlike statistical correlation measures, this dissimilarity measure reflects how human users quantify the changes in information retrieval result rankings. The DIR measure supports cognitive engineering tasks for information retrieval, such as work flow and interface design: using the measure, developers can identify which aspects of context heavily influence the outcome of the retrieval task and should therefore be in the focus of the user’s interaction with the system. The cognitive plausibility of DIR has been evaluated in two human participants tests, which demonstrate a strong correlation with user judgments.