A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> The world-wide web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the web. Moreover, different web sites often provide conflicting information on a subject, such as different specifications for the same product. In this paper we propose a new problem called Veracity, i.e., conformity to truth, which studies how to find true facts from a large amount of conflicting information on many subjects that is provided by various web sites. We design a general framework for the Veracity problem, and invent an algorithm called T ruth F inder , which utilizes the relationships between web sites and their information, i.e., a web site is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy web sites. Our experiments show that T ruth F inder successfully finds true facts among conflicting information, and identifies trustworthy web sites better than the popular search engines. <s> BIB001 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. ::: ::: We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources. <s> BIB002 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want to predict the truth values of the facts. Beyond simple methods such as voting (typically rather accurate), we explore techniques based on "corroboration", i.e., taking into account trust in the views. We introduce three fixpoint algorithms corresponding to different levels of complexity of an underlying probabilistic model. They all estimate both truth values of facts and trust in the views. We present experimental studies on synthetic and real-world data. This analysis illustrates how and in which context these methods improve corroboration results over baseline methods. We believe that corroboration can serve in a wide range of applications such as source selection in the semantic Web, data quality assessment or semantic annotation cleaning in social networks. This work sets the bases for a wide range of techniques for solving these more complex problems. <s> BIB003 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> Although much work in NLP has focused on simply determining what a document means, we also must know whether or not to believe it. Fact-finding algorithms attempt to identify the "truth" among competing claims in a corpus, but fail to take advantage of the user's prior knowledge and presume that truth itself is universal and objective rather than subjective. We introduce a framework for incorporating prior knowledge into any fact-finding algorithm, expressing both general "common-sense" reasoning and specific facts already known to the user as first-order logic and translating this into a tractable linear program. As our results show, this approach scales well to even large problems, both reducing error and allowing the system to determine truth respective to the user rather than the majority. Additionally, we introduce three new fact-finding algorithms capable of outperforming existing fact-finders in many of our experiments. <s> BIB004 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> Accessing online information from various data sources has become a necessary part of our everyday life. Unfortunately such information is not always trustworthy, as different sources are of very different qualities and often provide inaccurate and conflicting information. Existing approaches attack this problem using unsupervised learning methods, and try to infer the confidence of the data value and trustworthiness of each source from each other by assuming values provided by more sources are more accurate. However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective. In this paper we propose a semi-supervised approach that finds true values with the help of ground truth data. Such ground truth data, even in very small amount, can greatly help us identify trustworthy data sources. Unlike existing studies that only provide iterative algorithms, we derive the optimal solution to our problem and provide an iterative algorithm that converges to it. Experiments show our method achieves higher accuracy than existing approaches, and it can be applied on very huge data sets when implemented with MapReduce. <s> BIB005 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem. <s> BIB006 </s> A Survey on Truth Discovery <s> TRUTH DISCOVERY METHODS <s> 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. <s> BIB007
In the previous section, we summarize various truth discovery methods from five aspects, namely, input data, source reliability, object, claimed value, and the output. Now, we briefly describe several representative truth discovery methods, and compare them under different features (Tables 2  and 3) . By providing such a comparison, we hope to give some guidelines so that users and developers can choose an appropriate truth discovery method and apply it to the specific application scenarios. Due to space limitation, here we only describe each truth discovery algorithm briefly. For more details about these methods, the readers may refer to the reference papers. • TruthFinder BIB001 : In TruthFinder, Bayesian analysis is adopted to iteratively estimate source reliabilities and identify truths. The authors also propose the source consistency assumption and the concept of "implication", which are widely adopted in other truth discovery methods. • AccuSim BIB002 BIB007 : AccuSim also applies Bayesian analysis. In order to capture the similarity of claimed values, the implication function is adopted. • AccuCopy BIB002 BIB007 : This method improves AccuSim, and considers the copying relations among sources. The proposed method reduces the weight of a source if it is detected as a copier of other sources. • 2-Estimates BIB003 : In this approach, the single truth assumption is explored. By assuming that "there is one and only one true value for each object", this approach adopts complementary vote. • 3-Estimates BIB003 : 3-Estimates augments 2-Estimates by considering the difficulty of getting the truth for each object. • Investment BIB004 : In this approach, a source uniformly "invests" its reliability among its claimed values, and the confidence of a claimed value grows according to a non-linear function defined on the sum of invested reliabilities from its providers. Then the sources collect credits back from the confidence of their claimed values. • SSTF BIB005 : In this semi-supervised truth discovery approach, a small set of labeled truths are incorporated to guide the source reliability estimation. Meanwhile, both mutual exclusivity and mutual support are adopted to capture the relations among claimed values. • LTM BIB006 : LTM is a probabilistic graphical model which considers two types of errors under the scenarios of multiple truths: false positive and false negative. This enables LTM to break source reliability into two parameters, one for false positive error and the other for false negative error. • GTM  : GTM is a Bayesian probabilistic approach especially designed for solving truth discovery problems on continuous data.