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On the evaluation of Geographic Information Retrieval systems

Evaluation framework and case study

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International Journal on Digital Libraries Aims and scope Submit manuscript

Abstract

Search engines for Digital Libraries allow users to retrieve documents according to their contents. They process documents without differentiating the manifold aspects of information. Spatial and temporal dimensions are particularly dismissed. These dimensions are, however, of great interest for users of search engines targeting either the Web or specialized Digital Libraries. Recent studies reported that nearly 20% queries convey spatial and temporal information in addition to topical information. These three dimensions were referred to as parts of “geographic information.” In the literature, search engines handling those dimensions are called “Geographic Information Retrieval (GIR) systems.” Although several initiatives for evaluating GIR systems were undertaken, none was concerned with evaluating these three dimensions altogether. In this article, we address this issue by designing an evaluation framework, usefulness of which is highlighted through a case study involving a test collection and a GIR system. This framework allowed the comparison of our GIR system to state-of-the-art topical approaches. We also performed experiments for measuring performance improvement stemming from each dimension or their combination. We show that combining the three dimensions yields improvement in effectiveness (+73.9%) over a common topical baseline. Moreover, rather than conveying redundancy, the three dimensions complement each other.

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Abbreviations

ACF:

Absolute calendar feature

AP:

Average precision

ASF:

Absolute spatial feature

CF:

Calendar feature

DCG:

Definite clause grammar

GIR:

Geographic information retrieval

GIS:

Geographic information system

IE:

Information extraction

IR:

Information retrieval

IV:

Information visualization

MAP:

Mean average precision

NDCG:

Normalized discounted cumulative gain

NER:

Named entity recognition

NLP:

Natural language processing

RCF:

Relative calendar feature

RSF:

Relative spatial feature

SF:

Spatial feature

TF:

Term frequency

TFc:

Continuous term frequency

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Authors and Affiliations

  1. Université de Pau et des Pays de l’Adour, LIUPPA ÉA 3000, Avenue de l’Université, BP 1155, 64013, Pau cedex, France

    Damien Palacio & Christian Sallaberry

  2. Université de Toulouse, IRIT UMR 5505 CNRS, 118 route de Narbonne, 31062, Toulouse cedex 9, France

    Guillaume Cabanac & Gilles Hubert

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  4. Gilles Hubert
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Correspondence to Guillaume Cabanac.

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This paper is a substantially revised and extended version of a paper (with the title “Measuring Effectiveness of Geographic IR Systems in Digital Libraries: Evaluation Framework and Case Study”) originally appeared in the Proceedings of the 14th European Conference on Digital Libraries (ECDL 2010).

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Palacio, D., Cabanac, G., Sallaberry, C. et al. On the evaluation of Geographic Information Retrieval systems. Int J Digit Libr 11, 91–109 (2010). https://doi.org/10.1007/s00799-011-0070-z

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