A Survey on Truth Discovery <s> Input uncertainty <s> Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-theart fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems. <s> BIB001 </s> A Survey on Truth Discovery <s> Input uncertainty <s> Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories. We employ supervised machine learning methods for fusing these distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness. We report the results of multiple studies that explore the relative utility of the different information sources and extraction methods. <s> BIB002 </s> A Survey on Truth Discovery <s> Input uncertainty <s> The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: knowledge fusion. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods. <s> BIB003 </s> A Survey on Truth Discovery <s> Input uncertainty <s> We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the "Who wants to be a millionaire?" quiz show. Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd. By using weights optimized for reliability of participants (derived from the participants' confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy. Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems. <s> BIB004
When the observations are extracted from textual data (for example, in the knowledge fusion task BIB002 BIB003 ) or the sources provide observations with their confidence indicators (for example, in the question-answering system BIB004 ), it is necessary to consider the uncertainty of these observations. In BIB001 , the authors propose a way to generalize truth discovery methods, which considers multi-dimensional uncertainty, such as the uncertainty in information extractors.