Paper Info
Title
A Fuzzy Technique For On-Line Aggregation Of Pois From Social Media: Definition And Comparison With Off-Line Random-Forest Classifiers
Abstract
Social media represent an inexhaustible source of information concerning public places (also called points of interest (POIs)), provided by users. Several social media own and publish huge and independently-built corpora of data about public places which are not linked each other. An aggregated view of information concerning the same public place could be extremely useful, but social media are not immutable sources, thus the off-line approach adopted in all previous research works cannot provide up-to-date information in real time. In this work, we address the problem of on-line aggregating geo-located descriptors of public places provided by social media. The on-line approach makes impossible to adopt machine-learning (classification) techniques, trained on previously gathered data sets. We overcome the problem by adopting an approach based on fuzzy logic: we define a binary fuzzy relation, whose on-line evaluation allows for deciding if two public-place descriptors coming from different social media actually describe the same public place. We tested our technique on three data sets, describing public places in Manchester (UK), Genoa (Italy) and Stuttgart (Germany); the comparison with the off-line classification technique called "random forest" proved that our on-line technique obtains comparable results.
Year
DOI
Venue
2019
10.3390/info10120388
INFORMATION
Keywords
Field
DocType
fuzzy relation, geo-located places and POI, social media, on-line aggregation of POIs, random forest
Publication,Data set,Off line,Social media,Information retrieval,Computer science,Fuzzy logic,Artificial intelligence,Point of interest,Random forest,Machine learning,Binary number
Journal
Volume
Issue
Citations 
10
12
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Giuseppe Psaila1722192.45
Maurizio Toccu262.70