Multi-Source Spatial Entity Linkage

TY - GEN

T1 - Multi-Source Spatial Entity Linkage

AU - Isaj, Suela

AU - Zimányi, Esteban

AU - Pedersen, Torben Bach

PY - 2019/8/19

Y1 - 2019/8/19

N2 - Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. Location-based sources offer rich spatial information describing the semantics of locations. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities of interest, describe them with different attributes and sometimes provide contradicting information. Hence, the problem of finding which pairs of spatial entities belong to the same physical spatial entity demands specific attention. We propose a solution (QuadSky) to the problem of spatial entity linkage across diverse location-based sources. QuadSky starts with a spatial blocking technique (QuadFlex) that inherits the concept and the complexity from the quadtree algorithm but improves the splitting technique not to separate nearby points. After comparing the spatial entities of the same block, we propose a novel algorithm, referred to as SkyEx that separates the pairs considered as a match (positive class) from the rest (negative class) by using Pareto optimality. SkyEx does not require weights on the attributes, scoring function or a training set. QuadSky achieves 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, QuadSky provides the best trade-off between precision and recall and consequently, the best F-measure compared to the existing baselines.

AB - Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. Location-based sources offer rich spatial information describing the semantics of locations. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities of interest, describe them with different attributes and sometimes provide contradicting information. Hence, the problem of finding which pairs of spatial entities belong to the same physical spatial entity demands specific attention. We propose a solution (QuadSky) to the problem of spatial entity linkage across diverse location-based sources. QuadSky starts with a spatial blocking technique (QuadFlex) that inherits the concept and the complexity from the quadtree algorithm but improves the splitting technique not to separate nearby points. After comparing the spatial entities of the same block, we propose a novel algorithm, referred to as SkyEx that separates the pairs considered as a match (positive class) from the rest (negative class) by using Pareto optimality. SkyEx does not require weights on the attributes, scoring function or a training set. QuadSky achieves 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, QuadSky provides the best trade-off between precision and recall and consequently, the best F-measure compared to the existing baselines.

U2 - 10.1145/3340964.3340979

DO - 10.1145/3340964.3340979

M3 - Article in proceeding

SN - 978-1-4503-6280-1

SP - 1

EP - 10

BT - Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019

PB - Association for Computing Machinery

ER -