Rough membership function defines the measurement of relationship between
conditional and decision attribute from an Information system. In this paper we
propose a new method to construct rough graph through rough membership function
$\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with
imprecise and uncertain information. We explore the operations and properties
of rough graph in various stages of its structure.

本文提出了通过粗糙集近似度函数构建粗糙图的新方法，以便识别具有不精确和不确定信息的对象之间的模式，同时探讨了粗糙图在其结构的各个阶段中的操作和属性。

信息系统中处理不确定模式的粗糙图构建

Construction of Rough graph to handle uncertain pattern from an Information System

Uncertain information is being taken into account in an increasing number of
application fields. In the meantime, abduction has been proved a powerful tool
for handling hypothetical reasoning and incomplete knowledge. Probabilistic
logical models are a suitable framework to handle uncertain information, and in
the last decade many probabilistic logical languages have been proposed, as
well as inference and learning systems for them. In the realm of Abductive
Logic Programming (ALP), a variety of proof procedures have been defined as
well. In this paper, we consider a richer logic language, coping with
probabilistic abduction with variables. In particular, we consider an ALP
program enriched with integrity constraints `a la IFF, possibly annotated with
a probability value. We first present the overall abductive language, and its
semantics according to the Distribution Semantics. We then introduce a proof
procedure, obtained by extending one previously presented, and prove its
soundness and completeness.

本文讨论了使用带有变量的概率性假设建模的丰富逻辑语言进行概率性逆推的问题，提出了一种证明过程，该过程的有效性得到了证明。

具有概率完整性约束的非基于地面的非完整推理逻辑编程

Nonground Abductive Logic Programming with Probabilistic Integrity  Constraints

Chinese word segmentation is necessary to provide word-level information for
Chinese named entity recognition (NER) systems. However, segmentation error
propagation is a challenge for Chinese NER while processing colloquial data
like social media text. In this paper, we propose a model (UIcwsNN) that
specializes in identifying entities from Chinese social media text, especially
by leveraging ambiguous information of word segmentation. Such uncertain
information contains all the potential segmentation states of a sentence that
provides a channel for the model to infer deep word-level characteristics. We
propose a trilogy (i.e., candidate position embedding -> position selective
attention -> adaptive word convolution) to encode uncertain word segmentation
information and acquire appropriate word-level representation. Experiments
results on the social media corpus show that our model alleviates the
segmentation error cascading trouble effectively, and achieves a significant
performance improvement of more than 2% over previous state-of-the-art methods.

本文提出一种针对中文社交媒体的命名实体识别模型 (UIcwsNN)，通过利用词语切分的不确定性信息，实现对实体的识别，其使用了候选位置嵌入、位置选择性注意和自适应词卷积的三合一编码方式来获取合适的词层次表达，并在社交媒体语料库上得到了显著的性能提高。

将不确定的分割信息融入社交媒体中文 NER

Incorporating Uncertain Segmentation Information into Chinese NER for  Social Media Text

Answer Set Programming (ASP) is a popular framework for modeling
combinatorial problems. However, ASP cannot easily be used for reasoning about
uncertain information. Possibilistic ASP (PASP) is an extension of ASP that
combines possibilistic logic and ASP. In PASP a weight is associated with each
rule, where this weight is interpreted as the certainty with which the
conclusion can be established when the body is known to hold. As such, it
allows us to model and reason about uncertain information in an intuitive way.
In this paper we present new semantics for PASP, in which rules are interpreted
as constraints on possibility distributions. Special models of these
constraints are then identified as possibilistic answer sets. In addition,
since ASP is a special case of PASP in which all the rules are entirely
certain, we obtain a new characterization of ASP in terms of constraints on
possibility distributions. This allows us to uncover a new form of disjunction,
called weak disjunction, that has not been previously considered in the
literature. In addition to introducing and motivating the semantics of weak
disjunction, we also pinpoint its computational complexity. In particular,
while the complexity of most reasoning tasks coincides with standard
disjunctive ASP, we find that brave reasoning for programs with weak
disjunctions is easier.

本文介绍了新的 Possibilistic ASP (PASP) 模型语义，其中规则被解释为对可能性分布的约束，并且在这些约束的特殊模型中识别为可能主义答案集。此外，我们还发现一种新形式的析取，称为弱析取，其计算复杂性比传统析取容易。