Knowledge formalization for vector data matching using belief theory

Olteanu‐Raimond, Ana‐Maria; Mustière, Sébastien; Ruas, Anne

doi:10.5311/josis.2015.10.194

Cited by 28 publications

(25 citation statements)

References 31 publications

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“…This process is referred to as data matching. In this paper, we propose using a data matching algorithm defined by [34] to automatically detect homologous features between OSM and IGN POIs. The method, which uses belief theory, combines different criteria based on geometry, thematic and semantic properties.…”

Section: Assessment Based On Data Matching With Reference Datamentioning

confidence: 99%

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Touya¹,

Antoniou²,

Olteanu‐Raimond³

et al. 2017

IJGI

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Abstract:With the development of location-aware devices and the success and high use of Web 2.0 techniques, citizens are able to act as sensors by contributing geographic information. In this context, data quality is an important aspect that should be taken into account when using this source of data for different purposes. The goal of the paper is to analyze the quality of crowdsourced data and to study its evolution over time. We propose two types of approaches: (1) use the intrinsic characteristics of the crowdsourced datasets; or (2) evaluate crowdsourced Points of Interest (POIs) using external datasets (i.e., authoritative reference or other crowdsourced datasets), and two different methods for each approach. The potential of the combination of these approaches is then demonstrated, to overcome the limitations associated with each individual method. In this paper, we focus on POIs and places coming from the very successful crowdsourcing project: OpenStreetMap. The results show that the proposed approaches are complementary in assessing data quality. The positive results obtained for data matching show that the analysis of data quality through automatic data matching is possible but considerable effort and attention are needed for schema matching given the heterogeneity of OSM and the representation of authoritative datasets. For the features studied, it can be noted that change over time is sometimes due to disagreements between contributors, but in most cases the change improves the quality of the data.

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Section: Assessment Based On Data Matching With Reference Datamentioning

confidence: 99%

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Touya¹,

Antoniou²,

Olteanu‐Raimond³

et al. 2017

IJGI

Self Cite

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“…The simple unweighted sum [30,31] as well as decision-trees [37], logistic regression [26], entropy-based [27] and belief theory [2,38] methods have been tried in the past.…”

Section: Steps In the Matching Of Pois From Different Datasetsmentioning

confidence: 99%

“…Touya et al [2], as a quality assessment step, have matched subway stations and entrances POIs from OSM and from an authoritative dataset from Paris (France). They relied on a geographic data conflation method proposed by [38] which considers the POIs spatial distance as well as their name similarity measured by the normalized Levenshtein distance [41].…”

Section: Previous Work On Poi Matchingmentioning

confidence: 99%

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Novack

Peters

Zipf

2018

IJGI

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Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a "naive", though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method.

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“…A review of these studies shows that even though the overall performance in matching between features is satisfactory, there are some aspects that need to be improved. In this sense, the main problems are usually related to: the generation of false matching pairs in the cases of 1:n or n:m correspondences with polygons with relatively complex contours [29,30], the matching between networks with different levels of detail [33,34] and the process´s low level of automatization [32]. Especially interesting are the ideas presented by Mortara and Spagnolo [31].…”

mentioning

confidence: 99%

“…Overall, it can be said that the techniques employed to match objects using this type of algorithm are based on: the percentage of overlapped area [28][29][30], the context by means of a Delaunay triangulation [31,32], the belief theory on position and orientation [33] and probability classifiers over a set of "evidence", both geometric and attribute-based [34]. A review of these studies shows that even though the overall performance in matching between features is satisfactory, there are some aspects that need to be improved.…”

mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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Knowledge formalization for vector data matching using belief theory

Cited by 28 publications

References 31 publications

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

A Polygon and Point-Based Approach to Matching Geospatial Features

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