Catálogo de publicaciones - libros

Geospatial ontologies contain hierarchical structures, which are either based on the taxonomy of entity classes or functions and roles these entities can take. While the taxonomic hierarchies can be extracted from noun phrases contained in the formal texts that describe the geospatial domain, the hierarchies of action concepts can be traced from the verb phrases. This paper reports a simple case study of extracting the two types of such hierarchies from formal texts of traffic code. Problems of concurrent use of both hierarchies for ontology reasoning are dis-cussed, particularly, in context of the different views on geospatial ontologies. An approach based on separation of action and entity concepts. Use of probabilistic linkages between entities and actions is discussed as a way for integration of the two views. The initial results of this approach provide a first step towards an integration of the two existing views in the geospatial domain.

Maps are artifacts often derived from multiple sources of data, e.g., sensors, and processed by multiple methods, e.g., gridding and smoothing algorithms. As a result, complex metadata may be required to describe maps semantically. This paper presents an approach to describe maps by annotating associated provenance. Knowledge provenance can represent a semantic annotation mechanism that is more scalable than direct annotation of map. Semantic annotation of maps through knowledge provenance provides several benefits to end users. For example, a user study is presented showing that scientists with different levels of expertise and background are able to evaluate the quality of maps by analyzing their knowledge provenance information.

A major problem with encoding an ontology of geographic information in a formal language is how to cope with the issues of vagueness, ambiguity and multiple, possibly conflicting, perspectives on the same concepts. We present a means of structuring such an ontology which allows these issues to be handled in a controlled and principled manner, with reference to an example ontology of the domain of naive hydrography, and discuss some of the issues which arise when grounding such a theory in real data — that is to say, when relating qualitative geographic description to quantitative geographic data.

The integration and matching of geographic ontologies is a field in which many efforts are being employed. There are many proposals, addressing a diversity of features, both at the concept as at the instance-level. In order to make clear the issues that are involved in the matching process, in this paper we present the formal definition of the heterogeneities that may occur when comparing two geographic ontologies. Some of the heterogeneities are common to the ones found in conventional ontologies integration, and some others are specific for the geographic field. Furthermore, we discuss some still open issues, neglected up to now, but very important to achieve good results in a real scenario.

In this paper we describe an algorithm for merging ontologies from heterogeneous geographic data sources. The algorithm is based on an asymmetric similarity function that considers the spatial distribution of thematic values in the datasets. It has been used in the context of a semantic framework that provides a set of semantic services to enable external clients to find, translate and integrate thematic information from different geographic datasets in a repository. An optimised version of the algorithm is also described enabling its execution in real time, even with large datasets. The algorithm has been tested in the context of merging datasets with more than 10 spatial units.

In geospatial applications with heterogeneous classification schemes that describe related domains, an ontology-driven approach to data sharing and interoperability relies on the alignment of concepts across different ontologies. To enable scalability both in the size and the number of the ontologies involved, the alignment method should be automatic. In this paper, we propose two fully automatic alignment methods that use the structure of the ontology graphs for contextual information, thus providing the matching process with more semantics. We have tested our methods on a set of geospatial ontologies pertaining to the domain of wetlands and on four sets that belong to an ontology repository that is becoming the standard for testing ontology alignment techniques. We have compared the effectiveness and efficiency of the proposed methods against two previous approaches. The effectiveness results that we have obtained with at least one of the new methods are as good or better than the results obtained with the previously proposed methods.

Geographic Information Science community is recognized that modern Geographic Information Retrieval systems should support the processing of imprecise data distributed over heterogeneous repositories. This means the search for relevant geographic results for a geographic query () even if the data sources do not contain a result that matches exactly the user’s request and then approximated results would be useful. Therefore, GIR systems should be centred at the nature and essence of spatial data (their relations and properties) taken into consideration the user’s profile. Usually, semantic features are implicitly presented in data sources. In this work, we use three heterogeneous data sources: vector data, geographic ontology, and geographic dictionaries. These repositories usually store , , and of geographical objects under certain scenarios. In contrast to previous work, where these layers have been treated in an isolated way, their integration expects to be a better solution to capture the semantics of spatial objects. Thus, the use of spatial semantics and the integration of different information layers improve GIR, because adequate retrieval parameters according to the nature of spatial data, which emulate the user’s requirements, can be established. In particular, we use topological relations {}, semantic relations {}, and descriptions {}. An information extraction mechanism is designed for each data source, while the integration process is performed using the algorithm of ontology exploration. The ranking process is based on similarity measures, using the previously developed confusion theory. Finally, we present a case study to show some results of integrated GIR (iGIR) and compare them with Google’s ones in a tabular form.

SDIs offer access to a wealth of distributed data sources through standardised service interfaces. Recently, also geoprocessing capabilities are offered as services in SDIs. Combining data sources and processing services in service chains enable the generation of information that is tailored to the users’ needs. In this paper, we present a rule-based description framework and an associated discovery and composition method that helps service developers to create such service chains from existing services. The goal of the description framework is to describe services at a conceptual level rather than closely mirroring specific implementation details. It consists of a simple top-level ontology as well as a domain ontology, which provide the basic vocabulary for creating descriptions of both services and the information the service chain is to produce as a result. The composition method uses these descriptions to discover appropriate services and compose them into a service chain that can produce the required information. The method is illustrated using an example from the domain of risk management.

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 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.

Representing human spatial knowledge has long been a challenging research area. The objective of this paper is to model a route description of human navigation where verbal descriptions constitute the inputs of the modeling approach. We introduce a structural and logical model that applies graph principles to the representation of verbal route descriptions. The main assumption of this approach is that a route can be modeled as a path made of locations and actions, both being labeled by landmarks and spatial entities. This assumption is supported by previous studies and an experimentation made in natural environment that confirm the role of actions, landmarks and spatial entities in route descriptions. The modeling approach derives a logical and formal representation of a route description that facilitates the comprehension and analysis of its structural properties. It is supported by a graphic language, and illustrated by a preliminary prototype implementation applied to natural environments.