Multi-Dimensional Scattered Ranking Methods for Geographic Information Retrieval*

Kreveld, Marc van; Reinbacher, Iris; Arampatzis, Avi; Zwol, Roelof van

doi:10.1007/s10707-004-5622-6

Cited by 39 publications

(22 citation statements)

References 14 publications

(12 reference statements)

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“…Relevance in traditional web search is well-understood [22], even along multiple dimensions [2]. Geographic digital libraries [1,5,19] use geospatial metadata (e.g., coordinate representations of geographic objects) to support storage and retrieval of geographic information. A document's relevance may be computed in terms of its geospatial metadata's topological relation to a query (e.g., size, shape, location, distance) in addition to its content's relation to a search query [11].…”

Section: Related Workmentioning

confidence: 99%

Gander: Personalizing Search of the Here and Now

Michel

Julien

Payton

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. In Personalized Networked Spaces (PNets), people and devices are integrated with the environment and demand fluid interactions to enable connectivity to information, services, and people. PNet applications exhibit significant spatiotemporal demands in which connectivity to resources and information is personalized and focused on the here and now. We introduce Gander, a personalized search engine for the here and now. We examine how search expectations are affected when users and applications interact directly with the physical environment. We define a formal conceptual model of search in PNets that provides a clear definition of the framework and ultimately enables reasoning about relationships between search processing and the relevance of results. We assess our model by evaluating sophisticated Gander queries in a simulated PNet.

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Section: Related Workmentioning

confidence: 99%

Gander: Personalizing Search of the Here and Now

Michel

Julien

Payton

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…We guarantee that this value is 1.0 or less in order to make the integration of both spatial and textual relevances easier. In [35] both spatial and textual relevances are also normalized to values between 0 and 1. Finally, we compute the relevance of the document d with respect to the query q as the maximum relevance due to any location name (Equation 1).…”

Section: Pure Spatial Queriesmentioning

confidence: 99%

Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index

et al. 2010

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Both Geographic Information Systems and Information Retrieval have been very active research elds in the last decades. Lately, a new research eld called Geographic Information Retrieval has appeared from the intersection of these two elds.The main goal of this eld is to de ne index structures and techniques to e ciently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper two contributions to this research eld. First, we propose a new index structure that combines an inverted index and a spatial index based on an ontology of geographic space. This structure improves the query capabilities of other proposals. Then, we describe the architecture of a system for geographic information retrieval that de nes a work ow for the extraction of the geographic references in documents. The architecture also uses the index structure that we propose to solve pure spatial and textual queries as well as hybrid queries that combine both a textual and a spatial component. Furthermore, query expansion can be performed on geographic references because the index structure is based in an ontology.

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“…The example we use here actually uses coordinates which were stored in the police archive along with the data used in case 2. Van Kreveld et al (2005) consider the situation where a document is matched both according to its topicality and 28/36 also distance and consider these to be two dimensions of matching which can be represented visually. Here we consider only the geographical dimension of the problem but accept that the analysis performed in this case and case 2 would need to be integrated at some stage.…”

Section: Case 3: Suspect Prioritisation Based On Locationmentioning

confidence: 99%

Measuring the likelihood property of scoring functions in general retrieval models

Bache

Baillie

Crestani

2009

J. Am. Soc. Inf. Sci.

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Although retrieval systems based on probabilistic models will rank the objects (e.g. documents) being retrieved according to the probability of some matching criterion (e.g. relevance) they rarely yield an actual probability and the scoring function is interpreted to be purely ordinal within a given retrieval task. In this paper it is shown that some scoring functions possess the likelihood property, which means that the scoring function indicates the likelihood of matching when compared to other retrieval tasks which is potentially more useful than pure tanking although it cannot be interpreted as an actual probability. This property can be detected by using two modified effectiveness measure, entire precision and entire recall. Empirical evidence is offered to show the existence of this property both for traditional document retrieval and for analysis of crime data where suspects of an unsolved crime are ranked according to probability of culpability.

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Multi-Dimensional Scattered Ranking Methods for Geographic Information Retrieval*

Cited by 39 publications

References 14 publications

Gander: Personalizing Search of the Here and Now

Gander: Personalizing Search of the Here and Now

Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index

Measuring the likelihood property of scoring functions in general retrieval models

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