Matching Points of Interest from Different Social Networking Sites

Scheffler, Tatjana; Schirru, Rafael; Lehmann, Paul

doi:10.1007/978-3-642-33347-7_24

Cited by 34 publications

(34 citation statements)

References 4 publications

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“…Another example is provided by [32], who base their matching algorithm on the Euclidean distance, the name similarity, and the website similarity of two POI. [33] matches POI obtained from different social network sites by comparing their geographic distance as well as the string similarity of selected semantic attributes. Aiming to develop an assistive system for data editing, [34] compute the similarity of POI in OSM based on the change history of their respective tags.…”

Section: Methods For Poi Quality Assessmentmentioning

confidence: 99%

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

Jonietz

Zipf

2016

IJGI

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(1) Background: Due to the advent of Volunteered Geographic Information (VGI), large datasets of user-generated Points of Interest (POI) are now available. As with all VGI, however, there is uncertainty concerning data quality and fitness-for-use. Currently, the task of evaluating fitness-for-use of POI is left to the data user, with no guidance framework being available which is why this research proposes a generic approach to choose appropriate measures for assessing fitness-for-use of crowdsourced POI for different tasks. (2) Methods: POI are related to the higher-level concept of geo-atoms in order to identify and distinguish their two basic functions, geo-referencing and object-referencing. Then, for each of these functions, suitable measures of positional and thematic quality are developed based on existing quality indicators. (3) Results: Typical use cases of POI are evaluated with regards to their use of the two basic functions of POI, and allocated appropriate measures for fitness-for-use. The general procedure is illustrated on a brief practical example. (4) Conclusion: This research addresses the issue of fitness-for-use of POI on a higher conceptual level by relating it to more fundamental notions of geographical information representation. The results are expected to assist users of crowdsourced POI datasets in determining an appropriate method to evaluate fitness-for-use.

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Section: Methods For Poi Quality Assessmentmentioning

confidence: 99%

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

Jonietz

Zipf

2016

IJGI

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“…String or name similarity is a very effective, and therefore widely used, measure for matching POIs [13,26,28,31]. Most stores, restaurants, banks, cafés, gyms etc.…”

Section: Steps In the Matching Of Pois From Different Datasetsmentioning

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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“…Safra, et al [19] combined the spatial and non-spatial attributes of geospatial data and improved the existing location-based matching algorithms by using Pre-D, Post-R and Pre-F technologies. Scheffler, et al [20] used the spatial property as a fundamental filter and then combined the name metrics to match POIs from different social networking sites. To reflect the importance of property and set threshold flexibility, McKenzie proposed another heuristic approach that applies binomial logic regression [21] to assign weights and used the weighted multi-attributes model to find the corresponding objects.…”

Section: Related Workmentioning

confidence: 99%

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Xing

Xia

et al. 2016

Entropy

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Abstract:The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets.

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Matching Points of Interest from Different Social Networking Sites

Cited by 34 publications

References 4 publications

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

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

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

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