Kristin Stock

A semantic registry using a Feature Type Catalogue instead of ontologies to support spatial data infrastructures

The use of a semantically rich registry containing a Feature Type Catalogue (FTC) to represent the semantics of geographic feature types including operations, attributes and relationships between feature types is required to realise the benefits of Spatial Data Infrastructures (SDIs). Specifically, such information provides a more complete representation of the semantics of the concepts used in the SDI, and enables advanced navigation, discovery and utilisation of discovered resources. The presented approach creates an FTC implementation in which attributes, associations and operations for a given feature type are encapsulated within the FTC, and these conceptual representations are separated from the implementation aspects of the web services that may realise the operations in the FTC. This differs from previous approaches that combine the implementation and conceptual aspects of behaviour in a web service ontology, but separate the behavioural aspects from the static aspects of the semantics of the concept or feature type. These principles are demonstrated by the implementation of such a registry using open standards. The ebXML Registry Information Model (ebRIM) was used to incorporate the FTC described in ISO 19110 by extending the Open Geospatial Consortium ebRIM Profile for the Web Catalogue Service (CSW) and adding a number of stored queries to allow the FTC component of the standards‐compliant registry to be interrogated. The registry was populated with feature types from the marine domain, incorporating objects that conform to both the object and field views of the world. The implemented registry demonstrates the benefits of inheritance of feature type operations, attributes and associations, the ability to navigate around the FTC and the advantages of separating the conceptual from the implementation aspects of the FTC. Further work is required to formalise the model and include axioms to allow enhanced semantic expressiveness and the development of reasoning capabilities.

To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

A geospatial knowledge infrastructure consists of a set of interoperable components, including software, information, hardware, procedures and standards, that work together to support advanced discovery and creation of geoscientific resources, including publications, data sets and web services. The focus of the work presented is the development of such an infrastructure for resource discovery. Advanced resource discovery is intended to support scientists in finding resources that meet their needs, and focuses on representing the semantic details of the scientific resources, including the detailed aspects of the science that led to the resource being created. This paper describes an information model for a geospatial knowledge infrastructure that uses ontologies to represent these semantic details, including knowledge about domain concepts, the scientific elements of the resource (analysis methods, theories and scientific processes) and web services. This semantic information can be used to enable more intelligent search over scientific resources, and to support new ways to infer and visualise scientific knowledge. The work describes the requirements for semantic support of a knowledge infrastructure, and analyses the different options for information storage based on the twin goals of semantic richness and syntactic interoperability to allow communication between different infrastructures. Such interoperability is achieved by the use of open standards, and the architecture of the knowledge infrastructure adopts such standards, particularly from the geospatial community. The paper then describes an information model that uses a range of different types of ontologies, explaining those ontologies and their content. The information model was successfully implemented in a working geospatial knowledge infrastructure, but the evaluation identified some issues in creating the ontologies.

Identifying Semantically Similar Elements in Heterogeneous Spatial Databases Using Predicate Logic Expressions

For data to be successfully integrated, semantically similar database elements must be identified as candidates for merging. However, there may be significant differences between the concepts that participants in the integration exercise hold for the same real world entity. A possible method for identifying semantically similar elements prior to integration is based on cognitive science theory of concept attainment. The theory identifies inclusion rules as being the basis for the highest level of concept attainment, once concepts have been attained at lower, perceptive levels. Predicates can be used to combine inclusion rules as a basis for semantic representation of elements. The predicates for different database elements can then be compared to determine the similarities and differences between the elements. This information can be used to develop a set of semantically similar elements, and then to resolve representational conflicts between the elements prior to integration.

An approach to the management of multiple aligned multilingual ontologies for a geospatial earth observation system

Ontologies are widely used, within and outside the geospatial context to support semantic search that is capable of returning suitable resources. Some large, heterogeneous earth observation systems that are currently being developed in a multi-thematic environment require the support of multiple ontologies. Furthermore, some of the systems under current development operate in a multilingual environment, and it is desirable that multiple languages be supported by the systems themselves. This paper proposes a solution to this set of requirements using an architecture containing multiple and multilingual ontologies. Such ontologies are required to be related and the architecture described in this work, which adopts a spatial data infrastructure based on open geospatial standards, employs an algorithm for semantic search across the multiple multilingual ontologies aligned using the W3C Simple Knowledge Organization System (SKOS). It also provides an approach that is extendable by the addition of further ontologies if they are required for particular thematic purposes. A number of issues arose during phases of implementation, but the broad approach proved effective for supporting a large, heterogeneous, multilingual earth observation system.

The Logic of NEAR and FAR

We propose a new qualitative spatial logic based on metric (distance) relations between spatial objects. We provide a sound and complete axiomatisation of the logic with respect to metric models. The logic is intended for use in checking consistency of matching geospatial individuals from different data sets, where some data sets may be imprecise (e.g. crowd-sourced data).

eScience for Sea Science: A Semantic Scientific Knowledge Infrastructure for Marine Scientists

The COastal and Marine Perception Application for Scientific Scholarship (COMPASS) is a knowledge infrastructure that supports enhanced discovery of scientific resources, including publications, data sets and web services. It provides users with the ability to discover resources on the basis of domain knowledge using ontologies, and scientific knowledge, including the scientific models, theories and methods that were used to conduct the research described by the resource. The application includes an architecture that adopts standards from the geospatial information community to ensure interoperability between repositories and allow interaction with content from digital libraries. The architecture shows how ontologies can be used as a registry for an interoperable infrastructure. A prototype was successfully implemented and evaluated with users, finding enthusiasm and support for the approach, with some suggestions for improvements of the prototype implementation.

Finding Science with Science: Evaluating a Domain and Scientific Ontology User Interface for the Discovery of Scientific Resources

Current approaches to the discovery of scientific resources (publications, data sets and web services) are dominated by keyword search. These approaches do not allow scientists to search on the deeper semantics of scientific resources, or to discover resources on the basis of the scientific approaches taken. This article evaluates a user interface that allows users to discover scientific resources through structured knowledge in the form of ontologies describing the domain and the scientific knowledge inherent within the scientific resource, and also through informal user tags. These combined capabilities provide scientists with new and powerful options for resource discovery. A qualitative user evaluation explored how scientists felt about the approach for resource discovery in the context of their scientific work. The study showed that marine scientists were enthusiastic about the capabilities of such an approach and appreciated the ability to browse the visual structure of the knowledge and query on scientific method but, overall, preferred the use of tags over ontologies. The exploratory nature of the user study was used to identify future directions for such improvements.

Spatio‐temporal data management using object lifecycles: A case study of the Australian capital territory spatial data management system

The representation of time in spatial information systems allows historical data to be maintained and analysed, but increases system complexity. While many early methods for handling spatio‐temporality are limited or inefficient, more recent methods are expressive but complex. The object lifecycles method represents temporality using discrete stages in an objects life, focuses on the aspects of spatio‐temporality that are relevant to a specific land administration domain and is therefore simpler than the spatio‐temporal methods that are designed to handle all scenarios. The method has been successfully applied in a production environment: the ACT Spatial Data Management System.

Universality, language-variability and individuality: defining linguistic building blocks for spatial relations

Most approaches to the description of spatial relations for use in spatial querying attempt to describe a set of spatial relations that are universally understood by users. While this method has proved successful for expert users of geographic information, it is less useful for non-experts. Furthermore, while some work has implied the universal nature of spatial relations, a large amount of linguistic evidence shows that many spatial relations vary fundamentally across languages. Natural Semantic Metalanguage (NSM) is a body of linguistic research that has identified the few specific spatial relations that are universal across languages. We show how these spatial relations can be used to describe a range of more complex spatial relations, including some from non-Indo-European languages that cannot readily be described with the usual spatial operators. Thus we propose that NSM is a tool that may be useful for the development of the next generation of spatial querying tools, supporting multilingual environments with widely differing ways of talking about space.

Creating a Corpus of Geospatial Natural Language

The description of location using natural language is of interest for a number of research activities including the automated interpretation and generation of natural language to ease interaction with geographic information systems. For such activities, examples of geospatial natural language are usually collected from the personal knowledge of researchers, or in small scale collection activities specific to the project concerned. This paper describes the process used to develop a more generic corpus of geospatial natural language. The paper discusses the development and evaluation of four methods for semi-automated harvesting of geospatial natural language clauses from text to create a corpus of geospatial natural language. The most successful method uses a set of geospatial syntactic templates that describe common patterns of grammatical geospatial word categories and provide a precision of 0.66. Particular challenges were posed by the range of English dialects included, as well as metaphoric and sporting references.