The necessity to manage inconsistency in Description Logics Knowledge Bases
(KBs) has come to the fore with the increasing importance gained by the
Semantic Web, where information comes from different sources that constantly
change their content and may contain contradictory descriptions when considered
either alone or together. Classical reasoning algorithms do not handle
inconsistent KBs, forcing the debugging of the KB in order to remove the
inconsistency. In this paper, we exploit an existing probabilistic semantics
called DISPONTE to overcome this problem and allow queries also in case of
inconsistent KBs. We implemented our approach in the reasoners TRILL and BUNDLE
and empirically tested the validity of our proposal. Moreover, we formally
compare the presented approach to that of the repair semantics, one of the most
established semantics when considering DL reasoning tasks.

本文研究了利用概率语义来处理 Description Logics Knowledge Bases 中的不一致性问题，使之能够在查询时处理不一致的 KBs，且与传统的修复语义进行了比较。

利用不确定性查询不一致的描述逻辑知识库

Exploiting Uncertainty for Querying Inconsistent Description Logics  Knowledge Bases

Most of the existing knowledge graphs are not usually complete and can be
complemented by some reasoning algorithms. The reasoning method based on path
features is widely used in the field of knowledge graph reasoning and
completion on account of that its have strong interpretability. However,
reasoning methods based on path features still have several problems in the
following aspects: Path search isinefficient, insufficient paths for sparse
tasks and some paths are not helpful for reasoning tasks. In order to solve the
above problems, this paper proposes a method called DC-Path that combines
dynamic relation confidence and other indicators to evaluate path features, and
then guide path search, finally conduct relation reasoning. Experimental result
show that compared with the existing relation reasoning algorithm, this method
can select the most representative features in the current reasoning task from
the knowledge graph and achieve better performance on the current relation
reasoning task.

提出了一种名为 DC-Path 的方法，结合动态关系置信度和其他指标评估路径特征，并指导路径搜索，最终进行关系推理，实验结果表明，该方法在当前关系推理任务中能够从知识图谱中选择最具代表性的特征，并实现更好的性能。

基于动态关系置信度的知识图谱重要路径发现

Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence

We present a probabilistic extension of the description logic $\mathcal{ALC}$
for reasoning about statistical knowledge. We consider conditional statements
over proportions of the domain and are interested in the probabilistic-logical
consequences of these proportions. After introducing some general reasoning
problems and analyzing their properties, we present first algorithms and
complexity results for reasoning in some fragments of Statistical
$\mathcal{ALC}$.

本文针对统计知识推理的问题，提出了一种基于描述逻辑和概率论的扩展方法，并设计出多种算法来实现推理。