TY - GEN
T1 - On the discovery of evolving truth
AU - Li, Yaliang
AU - Li, Qi
AU - Gao, Jing
AU - Su, Lu
AU - Zhao, Bo
AU - Fan, Wei
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/10
Y1 - 2015/8/10
N2 - In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.
AB - In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.
KW - Dynamic data
KW - Source reliability
KW - Truth discovery
UR - http://www.scopus.com/inward/record.url?scp=84954135802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954135802&partnerID=8YFLogxK
U2 - 10.1145/2783258.2783277
DO - 10.1145/2783258.2783277
M3 - Conference contribution
AN - SCOPUS:84954135802
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 675
EP - 684
BT - KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Y2 - 10 August 2015 through 13 August 2015
ER -