Automatic POI Matching Using an Outlier Detection Based Approach

Almeida, Ana; Alves, Ana; Gomes, Rui

doi:10.1007/978-3-030-01768-2_4

Cited by 9 publications

(21 citation statements)

References 17 publications

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“…The similarity metrics considered in this study include spatial, name, structural, and extensional similarity. Another related study was conducted by Almedia et al [45], who proposed a POI matching approach based on an outlier detection model. The study began by identifying POI matches using the Factual Crosswalk API to connect the POIs from the Factual database with their Facebook and Foursquare counterparts before using them to train a machine learning (ML) model to perform outlier detection.…”

Section: Poi Matchingmentioning

confidence: 99%

“…The matching accuracy of the proposed POI matching approach is evaluated based on overall accuracy and balanced accuracy. While overall accuracy is a standard evaluation metric used frequently in past studies [39,42,45], the second evaluation metric (i.e., balanced accuracy) provides a more appropriate representation of the approach's performance by placing equal weights on the model's ability to identify both POI matches and non-matches during evaluation. This evaluation metric allows us to address the significant imbalance between the number of POI matches and non-matches usually found between POI datasets.…”

Section: Data Coverage and Completenessmentioning

confidence: 99%

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An End-to-End Point of Interest (POI) Conflation Framework

Low

Tekler

Cheah

2021

IJGI

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Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.

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Section: Poi Matchingmentioning

confidence: 99%

Section: Data Coverage and Completenessmentioning

confidence: 99%

An End-to-End Point of Interest (POI) Conflation Framework

Low

Tekler

Cheah

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…The similarity metrics considered in this study include spatial, name, structural, and extensional similarity. Another related study was conducted by Almedia et al [39], who proposed a POI matching approach based on an outlier detection model. The study began by identifying POI matches using the Factual Crosswalk API to connect the POIs from the Factual database with their Facebook and Foursquare counterparts before using them to train a machine learning (ML) model to perform outlier detection.…”

Section: Poi Matchingmentioning

confidence: 99%

“…The matching accuracy of the proposed POI matching approach is evaluated based on overall accuracy and balanced accuracy. While overall accuracy is a standard evaluation metric used frequently in past studies [33,36,39], the second evaluation metric (i.e., balanced accuracy) provides a more appropriate representation of the approach's performance by placing equal weights on the model's ability to identify both POI matches and non-matches during evaluation. This evaluation metric allows us to address the significant imbalance between the number of POI matches and non-matches usually found between POI datasets.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An End-to-end Point of Interest (POI) Conflation Framework

Low¹,

Tekler²,

Cheah³

2021

Preprint

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Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 minutes when matching over 12,000 POIs to result in 8,699 unique POIs, thereby demonstrating the framework's scalability for large scale implementation in dense urban contexts.

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“…In [5], the authors applied the Isolation Forest algorithm (one of the weakly supervised machine learning methods) [6]. The tagging of the training set uses the Factual Crosswalk API [7].…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic Points of Interest Matching

Piech

Smywiński-Pohl

Marcjan

et al. 2020

IJGI

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Complementing information about particular points, places, or institutions, i.e., so-called Points of Interest (POIs) can be achieved by matching data from the growing number of geospatial databases; these include Foursquare, OpenStreetMap, Yelp, and Facebook Places. Doing this potentially allows for the acquisition of more accurate and more complete information about POIs than would be possible by merely extracting the information from each of the systems alone. Problem: The task of Points of Interest matching, and the development of an algorithm to perform this automatically, are quite challenging problems due to the prevalence of different data structures, data incompleteness, conflicting information, naming differences, data inaccuracy, and cultural and language differences; in short, the difficulties experienced in the process of obtaining (complementary) information about the POI from different sources are due, in part, to the lack of standardization among Points of Interest descriptions; a further difficulty stems from the vast and rapidly growing amount of data to be assessed on each occasion. Research design and contributions: To propose an efficient algorithm for automatic Points of Interest matching, we: (1) analyzed available data sources—their structures, models, attributes, number of objects, the quality of data (number of missing attributes), etc.—and defined a unified POI model; (2) prepared a fairly large experimental dataset consisting of 50,000 matching and 50,000 non-matching points, taken from different geographical, cultural, and language areas; (3) comprehensively reviewed metrics that can be used for assessing the similarity between Points of Interest; (4) proposed and verified different strategies for dealing with missing or incomplete attributes; (5) reviewed and analyzed six different classifiers for Points of Interest matching, conducting experiments and follow-up comparisons to determine the most effective combination of similarity metric, strategy for dealing with missing data, and POIs matching classifier; and (6) presented an algorithm for automatic Points of Interest matching, detailing its accuracy and carrying out a complexity analysis. Results and conclusions: The main results of the research are: (1) comprehensive experimental verification and numerical comparisons of the crucial Points of Interest matching components (similarity metrics, approaches for dealing with missing data, and classifiers), indicating that the best Points of Interest matching classifier is a combination of random forest algorithm coupled with marking of missing data and mixing different similarity metrics for different POI attributes; and (2) an efficient greedy algorithm for automatic POI matching. At a cost of just 3.5% in terms of accuracy, it allows for reducing POI matching time complexity by two orders of magnitude in comparison to the exact algorithm.

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Automatic POI Matching Using an Outlier Detection Based Approach

Cited by 9 publications

References 17 publications

An End-to-End Point of Interest (POI) Conflation Framework

An End-to-End Point of Interest (POI) Conflation Framework

An End-to-end Point of Interest (POI) Conflation Framework

Towards Automatic Points of Interest Matching

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