TY - GEN
T1 - Using Markov Chain Monte Carlo to play Trivia
AU - Deutch, Daniel
AU - Greenshpan, Ohad
AU - Kostenko, Boris
AU - Milo, Tova
PY - 2011
Y1 - 2011
N2 - We introduce in this Demonstration a system called Trivia Masster that generates a very large Database of facts in a variety of topics, and uses it for question answering. The facts are collected from human users (the crowd); the system motivates users to contribute to the Database by using a Trivia Game, where users gain points based on their contribution. A key challenge here is to provide a suitable Data Cleaning mechanism that allows to identify which of the facts (answers to Trivia questions) submitted by users are indeed correct / reliable, and consequently how many points to grant users, how to answer questions based on the collected data, and which questions to present to the Trivia players, in order to improve the data quality. As no existing single Data Cleaning technique provides a satisfactory solution to this challenge, we propose here a novel approach, based on a declarative framework for defining recursive and probabilistic Data Cleaning rules. Our solution employs an algorithm that is based on Markov Chain Monte Carlo Algorithms.
AB - We introduce in this Demonstration a system called Trivia Masster that generates a very large Database of facts in a variety of topics, and uses it for question answering. The facts are collected from human users (the crowd); the system motivates users to contribute to the Database by using a Trivia Game, where users gain points based on their contribution. A key challenge here is to provide a suitable Data Cleaning mechanism that allows to identify which of the facts (answers to Trivia questions) submitted by users are indeed correct / reliable, and consequently how many points to grant users, how to answer questions based on the collected data, and which questions to present to the Trivia players, in order to improve the data quality. As no existing single Data Cleaning technique provides a satisfactory solution to this challenge, we propose here a novel approach, based on a declarative framework for defining recursive and probabilistic Data Cleaning rules. Our solution employs an algorithm that is based on Markov Chain Monte Carlo Algorithms.
UR - http://www.scopus.com/inward/record.url?scp=79957820913&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2011.5767941
DO - 10.1109/ICDE.2011.5767941
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:79957820913
SN - 9781424489589
T3 - Proceedings - International Conference on Data Engineering
SP - 1308
EP - 1311
BT - 2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
T2 - 2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Y2 - 11 April 2011 through 16 April 2011
ER -