Reality Mining on Micropost Streams. Deductive and Inductive Reasoning for Personalized and Location-based Recommendations

Tracking #: 440-1606

Marco Balduini
Irene Celino
Emanuele Della Valle
Yi Huang
Tony Lee
Seon-Ho Kim
Volker Tresp

Responsible editor: 
Aba-Sah Dadzie

Submission type: 
Full Paper
The rapid growth of personal opinions published in form of microposts, such as those found on Twitter, is the basis of novel emerging social and commercial services. In this paper, we describe BOTTARI, an augmented reality application that permits the personalized and localized recommendation of points of interest (POIs) based on the temporally-weighted opinions of the community. The technological basis of BOTTARI is the highly scalable LarKC platform for the rapid prototyping and devel- opment of Semantic Web applications. In particular, BOTTARI exploits LarKC’s deductive and inductive stream reasoning. We present an evaluation of BOTTARI based on a three year collection of tweets about 319 restaurants located in the 2 km2 district of Insadong, a popular tourist area of the South Korean city of Seoul. BOTTARI is the winner of the 9th edition of the Semantic Web Challenge, co-located with the 2011 International Semantic Web Conference. BOTTARI is currently field tested in Korea by Saltlux.
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