Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant of Dumpster-Shafer Theory, has been proposed to represent subjective opinions and to merge multiple opinions by offering a rich volume of fusing operators, which have been used to solve many opinion inference problems in trust networks. However, the operators of SL are known to be lack of scalability in inferring unknown opinions from large network data as a result of the sequential procedures of merging multiple opinions. In addition, SL does not consider deriving opinions in the presence of conflicting evidence. In this work, we propose a hybrid inference method that combines SL and Probabilistic Soft Logic (PSL), namely, Collective Subjective Plus, CSL + , which is resistible to highly conflicting evidence or a lack of evidence. PSL can reason a belief in a collective manner to deal with large-scale network data, allowing high scalability based on relationships between opinions. However, PSL does not consider an uncertainty dimension in a subjective opinion. To take benefits from both SL and PSL, we proposed a hybrid approach called CSL + for achieving high scalability and high prediction accuracy for unknown opinions with uncertainty derived from a lack of evidence and/or conflicting evidence. Through the extensive experiments on four semi-synthetic and two real-world datasets, we showed that the CSL + outperforms the state-of-the-art belief model (i.e., SL), probabilistic inference models (i.e., PSL, CSL), and deep learning model (i.e., GCN-VAE-opinion) in terms of prediction accuracy, computational complexity, and real running time.

中文翻译：

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中超+

使用不可靠的信息源产生相互矛盾的证据可能会导致很大的不确定性，这会严重损害决策过程。最近，已经采取了许多方法来整合来自多个来源的冲突数据和/或融合来自不同实体的冲突意见。为了明确地处理不确定性，一个信念模型称为主观逻辑(SL) 作为 Dumpster-Shafer 理论的一种变体，已被提出来表示主观意见并通过提供丰富的融合算子来合并多种意见，这些算子已被用于解决信任网络中的许多意见推断问题。然而，众所周知，由于合并多个意见的顺序过程，SL 的运营商在从大型网络数据中推断未知意见方面缺乏可扩展性。此外，SL 不考虑在存在相互矛盾的证据时得出意见。在这项工作中，我们提出了一种结合 SL 和概率软逻辑 (PSL) 的混合推理方法，即 Collective Subjective Plus，CSL+，这是对高度矛盾的证据或缺乏证据的抵抗力。PSL 可以以集体方式推理信念来处理大规模网络数据，允许基于意见之间的关系的高可扩展性。但是，PSL 没有考虑主观意见中的不确定性维度。为了从 SL 和 PSL 中获益，我们提出了一种称为 CSL 的混合方法+对于因缺乏证据和/或相互矛盾的证据而产生的不确定性的未知意见，实现高可扩展性和高预测准确性。通过对四个半合成数据集和两个真实世界数据集的广泛实验，我们证明了 CSL+在预测精度、计算复杂度方面优于最先进的信念模型（即 SL）、概率推理模型（即 PSL、CSL）和深度学习模型（即 GCN-VAE-opinion），和实际运行时间。