6,058 research outputs found
Voronoi-Based Region Approximation for Geographical Information Retrieval with Gazetteers
Gazetteers and geographical thesauri can be regarded as parsimonious spatial models that associate geographical location with place names and encode some semantic relations between the names. They are of particular value in processing information retrieval requests in which the user employs place names to specify geographical context. Typically the geometric locational data in a gazetteer are confined to a simple footprint in the form of a centroid or a minimum bounding rectangle, both of which can be used to link to a map but are of limited value in determining spatial relationships. Here we describe a Voronoi diagram method for generating approximate regional extents from sets of centroids that are respectively inside and external to a region. The resulting approximations provide measures of areal extent and can be used to assist in answering geographical queries by evaluating spatial relationships such as distance, direction and common boundary length. Preliminary experimental evaluations of the method have been performed in the context of a semantic modelling system that combines the centroid data with hierarchical and adjacency relations between the associated place names
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Combining link and content-based information in a Bayesian inference model for entity search
An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie
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
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