Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
ACM Computing Surveys (CSUR)
Data fusion: resolving data conflicts for integration
Proceedings of the VLDB Endowment
Information theory for data management
Proceedings of the VLDB Endowment
Integrating conflicting data: the role of source dependence
Proceedings of the VLDB Endowment
Truth discovery and copying detection in a dynamic world
Proceedings of the VLDB Endowment
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Probabilistic models to reconcile complex data from inaccurate data sources
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
A framework for corroborating answers from multiple web sources
Information Systems
Global detection of complex copying relationships between sources
Proceedings of the VLDB Endowment
Semi-supervised truth discovery
Proceedings of the 20th international conference on World wide web
A Bayesian approach to discovering truth from conflicting sources for data integration
Proceedings of the VLDB Endowment
An analysis of structured data on the web
Proceedings of the VLDB Endowment
Making better informed trust decisions with generalized fact-finding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Less is more: selecting sources wisely for integration
Proceedings of the VLDB Endowment
Truth finding on the deep web: is the problem solved?
Proceedings of the VLDB Endowment
Less is more: selecting sources wisely for integration
Proceedings of the VLDB Endowment
Truth finding on the deep web: is the problem solved?
Proceedings of the VLDB Endowment
Multi-source deep learning for information trustworthiness estimation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Compact explanation of data fusion decisions
Proceedings of the 22nd international conference on World Wide Web
Data fusion: resolving conflicts from multiple sources
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Extraction and integration of partially overlapping web sources
Proceedings of the VLDB Endowment
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The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in two domains where we believed data are fairly clean and data quality is important to people's lives: Stock and Flight. To our surprise, we observed a large amount of inconsistency on data from different sources and also some sources with quite low accuracy. We further applied on these two data sets state-of-the-art data fusion methods that aim at resolving conflicts and finding the truth, analyzed their strengths and limitations, and suggested promising research directions. We wish our study can increase awareness of the seriousness of conflicting data on the Web and in turn inspire more research in our community to tackle this problem.