Thanh Tam NguyenThanh Cong PhanQuoc Viet Hung NguyenAberer, KarlStantic, Bela2019-06-182019-06-182019-06-182019-08-0110.1016/j.inffus.2018.07.009https://infoscience.epfl.ch/handle/20.500.14299/157102WOS:000459364800005The Web became the central medium for valuable sources of information fusion applications. However, such user-generated resources are often plagued by inaccuracies and misinformation as a result of the inherent openness and uncertainty of the Web. While finding objective data is non-trivial, assessing their credibility with a high confidence is even harder due to the conflicts of information between Web sources. In this work, we consider the novel setting of fusing factual data from the Web with a credibility guarantee and maximal recall. The ultimate goal is that not only the information should be extracted as much as possible but also its credibility must satisfy a threshold requirement. To this end, we formulate the problem of instantiating a maximal set of factual information such that its precision is larger than a pre-defined threshold. Our proposed approach is a learning process to optimize the parameters of a probabilistic model that captures the relationships between data sources, their contents, and the underlying factual information. The model automatically searches for best parameters without pre-trained data. Upon convergence, the parameters are used to instantiate as much as factual information with a precision guarantee. Our evaluations of real-world datasets show that our approach outperforms the baselines up to 6 times.Computer Science, Artificial IntelligenceComputer Science, Theory & MethodsComputer Scienceinformation fusionknowledge extractionprecision guaranteeprobabilistic modelcredibility analysissentimentopinionstext::journal::journal article::research article