GeoBench

Morana, Anthony; Morel, Thomas; Berjawi, Bilal; Duchateau, Fabien

doi:10.1145/2666310.2666362

Cited by 8 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the blocking techniques in [73,74,76,90] that are not applied to spatial entities, there are some trivial spatial blocking techniques used in [80,91]. In [80], the spatial blocks are created using spatial entities at most 5 m apart.…”

Section: Spatial Entity Blockingmentioning

confidence: 99%

“…This threshold is user-defined without providing reasoning behind this choice. In contrast to [80], the threshold of the spatial distance in [91] is defined based on the type of spatial entities, small thresholds of 50 meters for bars and restaurants, but 500 meters for open spaces like parks. However, both works do not propose an automatic solution for spatial blocking, but rather offer arbitrary user-defined thresholds.…”

Section: Spatial Entity Blockingmentioning

confidence: 99%

“…The spatial entities have attributes such as name, categories, etc. For measuring these similarities, the traditional similarity metrics such as Levenshtein, Jaccard, Cosine [56,80,83,91] show good results. However, more advanced metrics can better capture the similarity of two entities such as a Soft-TFIDF with Levenshtein [92], and traditional string similarities trained further with supervised machine learning [93].…”

Section: Spatial Entity Comparisonmentioning

confidence: 99%

“…However, more advanced metrics can better capture the similarity of two entities such as a Soft-TFIDF with Levenshtein [92], and traditional string similarities trained further with supervised machine learning [93]. Even though these metrics are not tailored for spatial entities, they have shown promising results when applied to spatial entities [80,91]. Nevertheless, there are proposed solutions specifically designed for the name attributes of spatial entities which increase the accuracy of the matching process [94][95][96][97][98].…”

Section: Spatial Entity Comparisonmentioning

confidence: 99%

“…In addition to the name similarity, some of the works try to capture the semantic similarity of the entities [91] or learn it with a training set [102].…”

Section: Spatial Entity Comparisonmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Source Spatial Entity Extraction and Linkage

Isaj

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal -

show abstract

Section: Spatial Entity Blockingmentioning

confidence: 99%

Section: Spatial Entity Blockingmentioning

confidence: 99%

Section: Spatial Entity Comparisonmentioning

confidence: 99%

Section: Spatial Entity Comparisonmentioning

confidence: 99%

“…In addition to the name similarity, some of the works try to capture the semantic similarity of the entities [91] or learn it with a training set [102].…”

Section: Spatial Entity Comparisonmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Source Spatial Entity Extraction and Linkage

Isaj

View full text Add to dashboard Cite

show abstract

Conflating point of interest (POI) data: A systematic review of matching methods

Sun

et al. 2023

Computers, Environment and Urban Systems

View full text Add to dashboard Cite

Multi-Source Spatial Entity Linkage

Isaj

Pedersen

Zimányi

2022

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a time-efficient spatial blocking technique (QuadFlex), compares pairwise the spatial entities in the same block, ranks the pairs using Pareto optimality with the SkyRank algorithm, and finally, classifies the pairs with our novel SkyEx-* family of algorithms that yield 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the SkyEx-FES algorithm that explores only 27% of the skylines without any loss in F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates the optimal result with an F-measure loss of just 0.01. Finally, QuadSky provides the best trade-off between precision and recall, and the best F-measure compared to the existing baselines and clustering techniques, and approximates the results of supervised learning solutions.

show abstract

GeoBench

Cited by 8 publications

References 6 publications

Multi-Source Spatial Entity Extraction and Linkage

Multi-Source Spatial Entity Extraction and Linkage

Conflating point of interest (POI) data: A systematic review of matching methods

Multi-Source Spatial Entity Linkage

Contact Info

Product

Resources

About