2014
McKenzie, Grant; Janowicz, Krzysztof; Adams, Benjamin
A weighted multi-attribute method for matching user-generated Points of Interest Journal Article
In: Cartography and Geographic Information Science, vol. 41, pp. 125–137, 2014.
@article{10,
title = {A weighted multi-attribute method for matching user-generated Points of Interest},
author = {Grant McKenzie and Krzysztof Janowicz and Benjamin Adams},
url = {http://grantmckenzie.com/academics/McKenzie_CaGIS2014.pdf},
doi = {10.1080/15230406.2014.880327},
year = {2014},
date = {2014-01-01},
journal = {Cartography and Geographic Information Science},
volume = {41},
pages = {125–137},
abstract = {To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional 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 the different data sources; and second, attribute values have to be merged according to certain procedures which 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 Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes 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},
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To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional 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 the different data sources; and second, attribute values have to be merged according to certain procedures which 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 Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes 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
McKenzie, Grant; Klippel, Alexander
The Interaction of Landmarks and Map Alignment in You-Are-Here Maps Journal Article
In: The Cartographic Journal, 2014.
@article{11,
title = {The Interaction of Landmarks and Map Alignment in You-Are-Here Maps},
author = {Grant McKenzie and Alexander Klippel},
url = {http://grantmckenzie.com/academics/McKenzie_2015_YAH_preprint.pdf},
doi = {10.1080/00087041.2015.1108675},
year = {2014},
date = {2014-01-01},
journal = {The Cartographic Journal},
abstract = {Knowing where one is located within an environment is one of the most fundamental tasks humans have to master in their daily routines. Maps, as external representations of the environment offer intuitive ways to extend the capacities of the human cognitive systems. Operations such as planning a route can be performed on maps instead of in the environment. Question of how to design maps that support cognitive processes such as wayfinding in novel environments have been discussed in several disciplines. The research reported here addresses the question of how map alignment and the presence of landmarks in maps interact during wayfinding. For the purpose of systematically analyzing the relationship between map alignment and landmark presence, 9 virtual environments were designed. Routes learned from maps with different alignments and different numbers of landmarks present at decision points were used. While generally landmarks are assumed to foster wayfinding performance, our results indicate that misaligned maps can cancel out positive effects obtained through landmarks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Knowing where one is located within an environment is one of the most fundamental tasks humans have to master in their daily routines. Maps, as external representations of the environment offer intuitive ways to extend the capacities of the human cognitive systems. Operations such as planning a route can be performed on maps instead of in the environment. Question of how to design maps that support cognitive processes such as wayfinding in novel environments have been discussed in several disciplines. The research reported here addresses the question of how map alignment and the presence of landmarks in maps interact during wayfinding. For the purpose of systematically analyzing the relationship between map alignment and landmark presence, 9 virtual environments were designed. Routes learned from maps with different alignments and different numbers of landmarks present at decision points were used. While generally landmarks are assumed to foster wayfinding performance, our results indicate that misaligned maps can cancel out positive effects obtained through landmarks.
2013
McKenzie, Grant; Janowicz, Krzysztof; Adams, Benjamin
Weighted multi-attribute matching of user-generated points of interest Conference
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM ACM, Orlando, Florida, USA, 2013.
@conference{13,
title = {Weighted multi-attribute matching of user-generated points of interest},
author = {Grant McKenzie and Krzysztof Janowicz and Benjamin Adams},
url = {http://grantmckenzie.com/academics/McKenzie_Short_SIGSPATIAL2013.pdf},
year = {2013},
date = {2013-09-01},
booktitle = {Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
publisher = {ACM},
address = {Orlando, Florida, USA},
organization = {ACM},
abstract = {To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional 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 the different data sources; and second, attribute values have to be merged according to certain procedures which 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 Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes their conflation desirable. We present a weighted multi-attribute matching strategy and evaluate its performance. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional 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 the different data sources; and second, attribute values have to be merged according to certain procedures which 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 Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes their conflation desirable. We present a weighted multi-attribute matching strategy and evaluate its performance. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.
McKenzie, Grant; Adams, Benjamin; Janowicz, Krzysztof
A thematic approach to user similarity built on geosocial check-ins Conference
The 16th AGILE Conference on Geographic Information Science, Springer Springer, Leuven, Belgium, 2013.
@conference{14b,
title = {A thematic approach to user similarity built on geosocial check-ins},
author = {Grant McKenzie and Benjamin Adams and Krzysztof Janowicz},
url = {http://grantmckenzie.com/academics/McKenzie_AGILE2013.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {The 16th AGILE Conference on Geographic Information Science},
pages = {39–53},
publisher = {Springer},
address = {Leuven, Belgium},
organization = {Springer},
abstract = {Computing user similarity is key for personalized locationbased recommender systems and geographic information retrieval. So far, most existing work has focused on structured or semi-structured data to establish such measures. In this work, we propose topic modeling to exploit sparse, unstructured data, e.g., tips and reviews, as an additional feature to compute user similarity. Our model employs diagnosticity weighting based on the entropy of topics in order to assess the role of commonalities and variabilities between similar users. Finally, we offer a validation technique and results using data from the locationbased social network Foursquare.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Computing user similarity is key for personalized locationbased recommender systems and geographic information retrieval. So far, most existing work has focused on structured or semi-structured data to establish such measures. In this work, we propose topic modeling to exploit sparse, unstructured data, e.g., tips and reviews, as an additional feature to compute user similarity. Our model employs diagnosticity weighting based on the entropy of topics in order to assess the role of commonalities and variabilities between similar users. Finally, we offer a validation technique and results using data from the locationbased social network Foursquare.