Philip David Smart

A critical evaluation of ontology languages for geographic information retrieval on the Internet

A geo-ontology has a key role to play in the development of a spatially aware search engine, with regard to providing support for query disambiguation, query term expansion, relevance ranking and web resource annotation. This paper reviews those functions and identifies the challenges arising in the construction and maintenance of such an ontology. Two current contenders for the representation of the geo-ontology are GML, a specific markup language for geographic domains and OWL, a generic ontology representation language. Both languages are used to model the geo-ontology designed for supporting web retrieval of geographic concepts. The powers and limitations of the languages are identified. In particular, the paper highlights the lack of representation and reasoning abilities for different types of rules needed for supporting the geo-ontology.

Building place ontologies for the semantic web:: issues and approaches

Place geo-ontologies have a key role to play in the development of thegeospatial-semantic web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographicinformation and undergo a continuous process of revision and update. This papers reviews the limitations of the OWL ontology language for the representation of Place and proposes two novel approaches to frameworks that combine rules and OWL for building and managing Place ontologies.

Multi-source toponym data integration and mediation for a meta-gazetteer service

A variety of gazetteers exist based on administrative or user contributed data. Each of these data sources has benefits for particular geographical analysis and information retrieval tasks but none is a one fit all solution. We present a mediation framework to access and integrate distributed gazetteer resources to build a meta-gazetteer that generates augmented versions of place name information. The approach combines different aspects of place name data from multiple gazetteer sources that refer to the same geographic place and employs several similarity metrics to identify equivalent toponyms.

A framework for combining rules and geo-ontologies

Geo-ontologies have a key role to play in the development of the geospatial-semantic web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographic information and undergo a continuous process of revision and update. Hence, means of ensuring their integrity are crucial and needed to allow them to serve their purpose. This paper proposes the use of qualitative spatial reasoning as a tool to support the development of a geo-ontology management system. A new framework for the representation of and reasoning over geo-ontologies is presented using the web ontology language (OWL) and its associated reasoning tools. Spatial reasoning and integrity rules are represented using a spatial rule engine extension to the reasoning tools associated with OWL. The components of the framework are described and the implementation of the spatial reasoning engine is presented. This work is a step towards the realisation of a complete geo-ontology management system for the semantic web.

Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach

Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach.

Mining Topological Relations from the Web

Topological relations between geographic regions are of interest in many applications. When the exact boundaries of regions are not available, such relations can be established by analysing natural language information from Web documents. In particular, we demonstrate how redundancy-based techniques can be used to acquire containment and adjacency relations, and how fuzzy spatial reasoning can be employed to maintain the consistency of the resulting knowledge base.

Supporting Frameworks for the Geospatial Semantic Web

A lot of information on the web is geographically referenced. Discovering and linking this information poses eminent research challenges to the geospatial semantic web, with regards to the representation and manipulation of geographic data. Towards addressing these challenges, this work explores the potential of the current semantic web languages and tools. In particular, an integrated logical framework of rules and ontologies, using current W3C standards, is assessed for modeling geospatial ontologies of place encoding both symbolic and geometric references to place locations. Spatial reasoning is incorporated in the framework to facilitate the deduction of implicit semantics and for expressing spatial integrity constraints. The logical framework is then extended with geo-computation engines that offer more effective manipulations of geometric information. Example data sets mined from web resources are used to demonstrate and evaluate both frameworks, offering insights to their potentials and limitations.

Automatic image captioning from the web for GPS photographs

Increasing quantities of images are indexed by GPS coordinates. However, it is difficult to search within such pictures. In this paper, we propose a solution to automatically generate captions (including place name, keywords and summary) from the web content based on image location information. The richer descriptions have great potential to help image organisation, indexing and search. The solution is realised through the synergetic techniques from Geographic Information System, Web IR and multi-document summarisation.

Mining the web to detect place names

With the aim to improve the quality of gazetteers for geographic information retrieval systems, we present a method to detect place names employed by people submitting information to Web resources. We investigate how often people refer to a place using locative phrases in web queries and address the problem of defining cognitively significant place names. We propose Web mining as a means to decide whether a given particular named entity is in fact a place.

Interpreting spatial language in image captions

The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively.