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.

翻译：随着语义网的重要性日益凸显，管理描述逻辑知识库（KBs）中不一致性的需求逐渐显现。语义网中的信息来自不同来源，这些来源不断更新内容，且单独或整合时可能包含矛盾描述。经典推理算法无法处理不一致的知识库，迫使调试知识库以消除不一致性。本文利用名为DISPONTE的现有概率语义学解决此问题，允许在不一致知识库中执行查询。我们已在推理器TRILL和BUNDLE中实现该方法，并通过实验验证其有效性。此外，我们将所提出的方法与修复语义（描述逻辑推理任务中最成熟的语义之一）进行形式化比较。