A Survey on Truth Discovery <s> "Unknown" truths <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> BIB001 </s> A Survey on Truth Discovery <s> "Unknown" truths <s> When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. Truth discovery is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources. Without any prior knowledge, these questions, named no-truth questions, are difficult to be distinguished from the questions that have true answers, named has-truth questions. In particular, these no-truth questions degrade the precision of the answer integration system. We address such a challenge by introducing source quality, which is made up of three fine-grained measures: silent rate, false spoken rate and true spoken rate. By incorporating these three measures, we propose a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers. Moreover, since inferring this graphical model requires parameter tuning of the prior of truth, we propose an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate. Compared with existing methods, our method can effectively filter out no-truth questions, which results in more accurate source quality estimation. Consequently, our method provides more accurate and complete answers to both has-truth and no-truth questions. Experiments on three real-world datasets illustrate the notable advantage of our method over existing state-of-the-art truth discovery methods. <s> BIB002
In BIB001 , for each object, an extra candidate output "unknown" is considered. This technique is motivated by the following fact: When the relations among objects are represented as constraints, these constraints may contradict each other. Thus it is possible that no solution (truths for all the objects) can satisfy all the constraints. By introducing the output choice of "unknown", at least one feasible solution, "unknown" for all the objects, is promised. More importantly, for the objects that do not have a truth, the choice of "unknown" can be a correct output. For example, it is suitable to give an output of "unknown" for the questions (objects) about the death date of someone who is still alive. The authors in BIB002 propose a probabilistic model to take into account the truth existence, and the proposed method can assign the output of "unknown" to the questions that have no truths.