A Survey on Truth Discovery <s> Iterative 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> Iterative 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> Iterative 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> Iterative 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
In the general principle of truth discovery, the truth computation and source reliability estimation depend on each other. Thus some truth discovery methods BIB002 BIB003 BIB004 BIB001 are designed as iterative procedures, in which the truth computation step and source weight estimation step are iteratively conducted until convergence. In the truth computation step, sources' weights are as- BIB004 , the sources uniformly "invest" their reliabilities among their claimed values, and the truths are identified by weighted voting. To be more specific, each candidate value v receives the votes from sources in the following way: where Sv is the set of sources that provide this candidate value, and |Vs| is the number of claims made by source s. As the truths are identified by ranking the received votes, the final results {v * o }o∈O rely more on the sources with high weights. This follows the principle that the information from reliable sources will be counted more in the aggregation. In the source weight estimation step, source weights are estimated based on the current identified truths. Let's still take Investment for example. In truth computation step, the sources invest their reliabilities among claimed values, and now, in source weight computation step, they collect credits back from the identified truths as follows: That is, each source gets the proportional credits back from all its claimed values. As the received votes of values grow according to a non-linear function (Eq. (1)), the trustworthy information will have higher votes and contributes more to the source weight estimation. Thus the sources that provide trustworthy information more often will get more credits back and have higher weights.