A Survey on Truth Discovery <s> Source reliability initialization <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 However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective.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> BIB001 </s> A Survey on Truth Discovery <s> Source reliability initialization <s> We are often thrilled by the abundance of information surrounding us and wish to integrate data from as many sources as possible. However, understanding, analyzing, and using these data are often hard. Too much data can introduce a huge integration cost, such as expenses for purchasing data and resources for integration and cleaning. Furthermore, including low-quality data can even deteriorate the quality of integration results instead of bringing the desired quality gain. Thus, "the more the better" does not always hold for data integration and often "less is more". ::: ::: In this paper, we study how to select a subset of sources before integration such that we can balance the quality of integrated data and integration cost. Inspired by the Marginalism principle in economic theory, we wish to integrate a new source only if its marginal gain, often a function of improved integration quality, is higher than the marginal cost, associated with data-purchase expense and integration resources. As a first step towards this goal, we focus on data fusion tasks, where the goal is to resolve conflicts from different sources. We propose a randomized solution for selecting sources for fusion and show empirically its effectiveness and scalability on both real-world data and synthetic data. <s> BIB002 </s> A Survey on Truth Discovery <s> Source reliability initialization <s> Information Extraction using multiple information sources and systems is beneficial due to multisource/system consolidation and challenging due to the resulting inconsistency and redundancy. We integrate IE and truth-finding research and present a novel unsupervised multi-dimensional truth finding framework which incorporates signals from multiple sources, multiple systems and multiple pieces of evidence by knowledge graph construction through multi-layer deep linguistic analysis. Experiments on the case study of Slot Filling Validation demonstrate that our approach can find truths accurately (9.4% higher F-score than supervised methods) and efficiently (finding 90% truths with only one half the cost of a baseline without credibility estimation). <s> BIB003 </s> A Survey on Truth Discovery <s> Source reliability initialization <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> BIB004
Most of truth discovery methods start with uniform weights among all sources. As a result, the performance of truth discovery may rely on the majority. When the majority of sources are good, this strategy works well. However, the real scenarios usually are not the case, as sources may copy information from others or they may provide out-of-date information. Nowadays, people apply truth discovery on challenging tasks, such as information extraction and knowledge graph construction. In these challenging tasks, most of the sources are unreliable. For example, BIB003 reports that in their task, "62% of the true responses are produced only by 1 or 2 of the 18 systems (sources)". This example reveals that a better initialization for source reliability is much in demand. Recent work adopts a subset of labeled data BIB002  BIB001 , an external trustful information source BIB004 , or the similarity among sources BIB003 as prior knowledge to initialize (or help to initialize) the source reliability.