Evaluating Geographical Knowledge Re-Ranking, Linguistic Processing and Query Expansion Techniques for Geographical Information Retrieval

Ferrés, Daniel; Rodríguez, Horacio

doi:10.1007/978-3-319-23826-5_30

Cited by 5 publications

(15 citation statements)

References 12 publications

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“…However, a major challenge remains the limited availability of training data outside of commercial search contexts. Ferrès and Rodriguez, 2015 demonstrate the possibilities of using existing training data, in their case from the GeoCLEF evaluation campaign discussed in detail in Chapter 7 on evaluation, to improve over results through more intelligent ranking.…”

Section: Learning To Rank For Girmentioning

confidence: 99%

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Purves

Clough

Jones

et al. 2018

FNT in Information Retrieval

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Significant amounts of information available today contain references to places on earth. Traditionally such information has been held as structured data and was the concern of Geographic Information Systems (GIS). However, increasing amounts of data in the form of unstructured text are available for indexing and retrieval that also contain spatial references. This monograph describes the field of Geographic Information Retrieval (GIR) that seeks to develop spatially-aware search systems and support user's geographical information needs. Important concepts with respect to storing, querying and analysing geographical information in computers are introduced, before user needs and interaction in the context of GIR are explored. The task of associating documents with coordinates, prior to their indexing and ranking forms the core of any GIR system, and different approaches and their implications are discussed. Evaluating the resulting systems and their components, and different paradigms for doing so continue to be an important area of research in GIR and are illustrated through a number of examples. The article concludes by setting out a range of future challenges for research in this field.

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Section: Learning To Rank For Girmentioning

confidence: 99%

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Purves

Clough

Jones

et al. 2018

FNT in Information Retrieval

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“…Related to the query description improvement, other works have focused on the identification of textual patterns describing locations or location-based references (e.g., north of X ). Works such as Sallaberry (2013), Ferrés and Rodríguez (2015), and Kim et al (2016) show that the identification of textual patterns describing locations can greatly improve the quality of the results when spatial description in metadata records is textual. However, in geospatial data catalogs these solutions are less relevant because, by design, the metadata records of spatial resources specify their spatial limits as coordinates.…”

Section: State Of the Artmentioning

confidence: 99%

Aggregation-based information retrieval system for geospatial data catalogs

Lacasta

López-Pellicer

Espejo-García

et al. 2017

International Journal of Geographical Information Science

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Geospatial data catalogs enable users to discover and access geographical information. Prevailing solutions are document-oriented and fragment the spatial continuum of the geospatial data into independent and disconnected resources described through metadata. Due to this, the complete answer for a query may be scattered across multiple resources, making its discovery and access more difficult. This paper proposes an improved information retrieval process for geospatial data catalogs that aggregates the search results by identifying the implicit spatial/thematic relations between the metadata records of the resources. These aggregations are constructed in such a way that they match better the user query than each resource individually.

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“…Sets of place names are firstly used to represent the document extents and the relevance is calculated by their hierarchical relationships [21,22]. Furthermore, geographic ontologies or knowledge graphs, instead of footprints, are constructed to measure the semantic similarities for spatial rankings [23][24][25][26]. The latest topic models are also utilized in geographic information retrieval to find the similar place-related documents based on latent semantics [23,27].…”

Section: Introductionmentioning

confidence: 99%

A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval

Gao

Jiang

Zhong

et al. 2016

IJGI

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Abstract:In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios.

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Evaluating Geographical Knowledge Re-Ranking, Linguistic Processing and Query Expansion Techniques for Geographical Information Retrieval

Cited by 5 publications

References 12 publications

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Aggregation-based information retrieval system for geospatial data catalogs

A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval

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