A weighted multi-attribute method for matching user-generated Points of Interest

Bibliographic Data

ID12750683
AuthorsGemma Mckenzie (0000-0003-3247-2777, University of California, Santa Barbara, corresponding author), Krzysztof Janowicz (0009-0003-1968-887X, University of California, Santa Barbara), Benjamin Adams (0000-0002-1657-9809, University of Auckland)
Year2014
Volume41
Issue2
Pages125-137
Publication date2014-01-30
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueCartography and Geographic Information Science (JOURNAL)
Journal identifiersISSN: 1523-0406 • E-ISSN: 1545-0465
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/15230406.2014.880327
OpenAlexW1971375508
LanguageEN
Citations received20
References cited18

To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multi-dimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two-step process. First, one has to establish identity relations between information entities across different data sources; and second, attribute values have to be merged according to certain procedures that avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interest (POI) from the Location-based Social Network Foursquare and the local directory service Yelp. While both contain overlapping attributes that can be used for matching, they have specific strengths and weaknesses that make their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities

Conflation · Data mining · Exploit · Information retrieval · Matching (statistics · Point of interest · Similarity (geometry · Variety (cybernetics · Computer Science · Data Management and Algorithms · Data Quality and Management · Geographic Information Systems Studies · Mathematics · Artificial Intelligence

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