A Fuzzy Technique for On-Line Aggregation of POIs from Social Media: Definition and Comparison with Off-Line Random-Forest Classifiers

Psaila, Giuseppe; Toccu, Maurizio

doi:10.3390/info10120388

Cited by 6 publications

(26 citation statements)

References 27 publications

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“…While the authors claimed that the approach is scalable when applied to larger areas, the claim may not hold when conflating multiple POI sources as it will increase the number of potential edges that can be formed between each node. Another study conducted by Psaila and Toccu [43] proposed an approach based on fuzzy logic and possibility theory to perform online aggregation of POIs from Google Places and Facebook. The proposed approach measures the degree of likelihood between two place descriptors, containing information about the location name, address, and geographic coordinates, to evaluate if they refer to the same location.…”

Section: Poi Matchingmentioning

confidence: 99%

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%

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

Low

Tekler

Cheah

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…While the authors claimed that the approach is scalable when applied to larger areas, the claim may not hold when conflating multiple POI sources as it will increase the number of potential edges that can be formed between each node. Another study conducted by Psaila and Toccu [37] proposed an approach based on fuzzy logic and possibility theory to perform online aggregation of POIs from Google Places and Facebook. The proposed approach measures the degree of likelihood between two place descriptors, containing information about the location name, address, and geographic coordinates, to evaluate if they refer to the same location.…”

Section: Poi Matchingmentioning

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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“…During the test phase, all the classification tress are independently used to classify the unclassified case: the class assigned by the majority of trees is chosen as class label assigned to the unclassified case. This technique is very general and widely used in many application contexts, not only for text classification [45].…”

Section: Classification Techniquesmentioning

confidence: 99%

Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

et al. 2020

Self Cite

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Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users post many messages, because they aim at being recognized as influencers, typically on specific topics. How a user can discover influencers concerned with her/his interest? Micro-blog apps and web sites lack a functionality to recommend users with influencers, on the basis of the content of posted messages. In this paper, we envision such a scenario and we identify the problem that constitutes the basic brick for developing a recommender of (possibly influencer) users: training a classification model by exploiting messages labeled with topical classes, so as this model can be used to classify unlabeled messages, to let the hidden topic they talk about emerge. Specifically, the paper reports the investigation activity we performed to demonstrate the suitability of our idea. To perform the investigation, we developed an investigation framework that exploits various patterns for extracting features from within messages (labeled with topical classes) in conjunction with the mostly-used classifiers for text classification problems. By means of the investigation framework, we were able to perform a large pool of experiments, that allowed us to evaluate all the combinations of feature patterns with classifiers. By means of a cost-benefit function called “Suitability”, that combines accuracy with execution time, we were able to demonstrate that a technique for discovering topics from within messages suitable for the application context is available.

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A Fuzzy Technique for On-Line Aggregation of POIs from Social Media: Definition and Comparison with Off-Line Random-Forest Classifiers

Cited by 6 publications

References 27 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 the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

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