A Survey on Truth Discovery <s> Output explanation <s> A frequent problem when dealing with data gathered from multiple sources on the web (ranging from booksellers to Wikipedia pages to stock analyst predictions) is that these sources disagree, and we must decide which of their (often mutually exclusive) claims we should accept. Current state-of-the-art information credibility algorithms known as "fact-finders" are transitive voting systems with rules specifying how votes iteratively flow from sources to claims and then back to sources. While this is quite tractable and often effective, fact-finders also suffer from substantial limitations; in particular, a lack of transparency obfuscates their credibility decisions and makes them difficult to adapt and analyze: knowing the mechanics of how votes are calculated does not readily tell us what those votes mean, and finding, for example, that a source has a score of 6 is not informative. We introduce a new approach to information credibility, Latent Credibility Analysis (LCA), constructing strongly principled, probabilistic models where the truth of each claim is a latent variable and the credibility of a source is captured by a set of model parameters. This gives LCA models clear semantics and modularity that make extending them to capture additional observed and latent credibility factors straightforward. Experiments over four real-world datasets demonstrate that LCA models can outperform the best fact-finders in both unsupervised and semi-supervised settings. <s> BIB001 </s> A Survey on Truth Discovery <s> Output explanation <s> Despite the abundance of useful information on the Web, different Web sources often provide conflicting data, some being out-of-date, inaccurate, or erroneous. Data fusion aims at resolving conflicts and finding the truth. Advanced fusion techniques apply iterative MAP (Maximum A Posteriori) analysis that reasons about trustworthiness of sources and copying relationships between them. Providing explanations for such decisions is important for a better understanding, but can be extremely challenging because of the complexity of the analysis during decision making. This paper proposes two types of explanations for data-fusion results: snapshot explanations take the provided data and any other decision inferred from the data as evidence and provide a high-level understanding of a fusion decision; comprehensive explanations take only the data as evidence and provide an in-depth understanding of a fusion decision. We propose techniques that can efficiently generate correct and compact explanations. Experimental results show that (1) we generate correct explanations, (2) our techniques can significantly reduce the sizes of the explanations, and (3) we can generate the explanations efficiently. <s> BIB002
The interpretations of truth discovery results are important for both end-users ("why should I trust the results") and developers ("how to improve the results"). However, most of the truth discovery methods output opaque information. For example, the output could be that the reliability degree of a source is 6, or the support from sources for a claimed value is 3.7. These intermediate parameters are not interpretable for end-users and developers, and we need to construct some intuitive explanations based on the raw outputs. In BIB001 , source reliability degree is modeled as the probability of a source asserting the truth, which is more meaningful for human interpretation. In addition, they extend the source reliability definition by considering the difference between knowing the truths and telling the truths. Specifically, in their SimpleLCA model, the reliability is considered as the probability of a source asserting the truths, while in GuessLCA model the reliability is the probability of a source both knowing and asserting the truths, and in MistakeLCA and LieLCA models, the reliability means the probability of a source intending to tell the truths. In BIB002 , the authors propose methods to automatically generate both snapshot explanation and comprehensive explanation for truth discovery. These explanations could be used to explain the results of truth discovery derived from iterative computing procedure. Snapshot explanation provides endusers a high-level understanding of the results by gathering all the positive and negative explanations for a specific decision over alternative choices. It lists all the evidences that support the decision as positive explanations and all the evidences which are against the decision as negative ones. In order to make the explanation more concise, categorization and aggregation are conducted so that similar evidences will not appear repeatedly. In addition, they perform evidence list shortening to remove evidence with less importance, and thus human-interpretable explanations can be derived. Compared with snapshot explanation, comprehensive explanation constructs a directed acyclic graph (DAG) to provide an in-depth explanation of the results. The DAG is constructed by tracing the decisions from the last round of iteration all the way to the first iteration where each node represents a decision and the children represent evidences. After the DAG construction, an explanation shortening is performed to eliminate those rounds where no decisions are reached to derive a concise explanation.