Introduction and Motivation

1. 1Department of Computer Science and Engineering • The Pennsylvania State University • {mxy177,dus212,wlee,pzy102}@cse.psu.edu • 2Department of Geography • University of California, Santa Barbara • {jano}@geog.ucsb.edu • Tags are missing • In our Foursquare and Whirrl dataset, there are a lot of places missing tags • Location-based Social Networks • E.g., Facebook Place and Foursquare • User check-in places • Tags are important • Business categorization • Location search • Place recommendation • Data cleaning • … Places missing tags Places missing tags Places with tags 33% Places with tags On the Semantic Annotation of Places in Location-Based Social NetworksMao Ye1, Dong Shou1, Wang-Chien Lee1, Peifeng Yin1, Krzysztof Janowicz2 32% 67% 68% • Foursquare • Whrrl Problem Description SAP Framework • Place semantic annotation (SAP) problem • Multi-label classification problem • Input • User check-in logs <who, where, when> • Some places are tagged • Output • Infer tags for the rest places Check-in logs Binary Classifier For tag t1 Feature Extraction (FE) Component Binary Classifier For tag t2 Place • Feature extraction (FE) • Check-in logs  features • Features to describe a place Binary Classifier For tag tm Introduction and Motivation FE- Explicit Pattern FE- Implicit Relatedness Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 00:00 Bars Bars ? Gym Health Beauty Restaurant Restaurant Restaurant Restaurant Restaurant Restaurant Restaurant Spa Shopping Shopping Restaurant Shopping Restaurant Restaurant 23:59 Bars Places checked in by the same user at around the same time (not necessarily the same day) are probably in the same category Evaluation • Whrrl.com. 5,892 users, 53,432 places and 199 types of tags • According to Yelp, we map 199 tags into 21 categories. • Comparison: EP, IR and SAP (EP+IR)