Geographic information retrieval: Modeling uncertainty of user's context

Bordogna, Gloria; Ghisalberti, Giorgio; Psaila, Giuseppe

doi:10.1016/j.fss.2011.04.005

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…2.4.1. From "Place" to "Space": Representing Uncertainty of Geoinformation within Texts to Support Geographic Information Retrieval In [163], a GIR system was proposed that allows for extracting implicit geosemantics within contents of textual documents through the identification of fuzzy geographic footprints, i.e., the distinct locations on Earth referred to by documents.…”

Section: Implicit Geosemanticsmentioning

confidence: 99%

Implicit, Formal, and Powerful Semantics in Geoinformation

Bordogna

Fugazza

d’Aragona

et al. 2021

IJGI

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Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics.

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Section: Implicit Geosemanticsmentioning

confidence: 99%

Implicit, Formal, and Powerful Semantics in Geoinformation

Bordogna

Fugazza

d’Aragona

et al. 2021

IJGI

Self Cite

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“…Then, we compared the retrieval results between semantic retrieval and the traditional retrieval methods, which were mainly keyword-matching techniques. In general, geographic information retrieval concerns the retrieval of thematically and geographically relevant information resources [64][65][66]. A GIR facility must evaluate the relatedness in terms of both the theme and spatial location (sometimes the temporal similarity is also evaluated, but it is not the most common situation).…”

Section: Application Of Tlrmmentioning

confidence: 99%

An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources

Chen

Song

Yang

2018

IJGI

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Abstract:In geographic information science, semantic relatedness is important for Geographic Information Retrieval (GIR), Linked Geospatial Data, geoparsing, and geo-semantics. But computing the semantic similarity/relatedness of geographic terminology is still an urgent issue to tackle. The thesaurus is a ubiquitous and sophisticated knowledge representation tool existing in various domains. In this article, we combined the generic lexical database (WordNet or HowNet) with the Thesaurus for Geographic Science and proposed a thesaurus-lexical relatedness measure (TLRM) to compute the semantic relatedness of geographic terminology. This measure quantified the relationship between terminologies, interlinked the discrete term trees by using the generic lexical database, and realized the semantic relatedness computation of any two terminologies in the thesaurus. The TLRM was evaluated on a new relatedness baseline, namely, the Geo-Terminology Relatedness Dataset (GTRD) which was built by us, and the TLRM obtained a relatively high cognitive plausibility. Finally, we applied the TLRM on a geospatial data sharing portal to support data retrieval. The application results of the 30 most frequently used queries of the portal demonstrated that using TLRM could improve the recall of geospatial data retrieval in most situations and rank the retrieval results by the matching scores between the query of users and the geospatial dataset.

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“…A new model for the geographic information retrieval was proposed in (Bordogna et al, 2012) and implemented the system to represent uncertainty in indexing the geographic contents and the user's perspective and preferences during the manipulating the spatial quires. To denote the geographic content authors used fuzzy footprints.…”

Section: Jcsmentioning

confidence: 99%

Efficacious Geospatial Information Retrieval Using Density Probabilistic Document Correlation Approach

Uma¹,

Muneeswaran²

2013

Journal of Computer Science

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Information Retrieval (IR) is a profound technique to find information that addresses the need of query. Processing of normal text is easier and information can be retrieved efficiently. There are plenty of algorithms in hand to carry out the normal text retrieval. Whereas retrieving geospatial information is very complex and requires additional operations to be performed. Since geospatial data contain complex details than general data such as location, direction. To handle geographical queries, we proposed a Density Probabilistic Document Correlation (DPDC) approach. This approach, initially categorize the geographical features from text that satisfies the given queries. Existing text classification techniques are unsuitable for geospatial text classification due to the exclusivity of the geographical features. Depending on the DPDC approach result we predict overlap of the feature set for a document. Based on overlap and document correlation, the documents are ranked. Highly relevant documents are extracted depending on the score obtained through ranking. Documents with high score are considered the most relevant. The experimental results show that our proposed method efficiently retrieves the list of relevant documents.

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Geographic information retrieval: Modeling uncertainty of user's context

Cited by 22 publications

References 16 publications

Implicit, Formal, and Powerful Semantics in Geoinformation

Implicit, Formal, and Powerful Semantics in Geoinformation

An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources

Efficacious Geospatial Information Retrieval Using Density Probabilistic Document Correlation Approach

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