Knowledge bases (KBs) are not static entities: new information constantly appears and some of the previous knowledge becomes obsolete. In order to reflect this evolution of knowledge, KBs should be expanded with the new knowledge and contracted from the obsolete one. This problem is well-studied for propositional but much less for first-order KBs. In this work we investigate knowledge expansion and contraction for KBs expressed in $\mathit{DL}\text{-}\mathit{Lite}$, a family of description logics (DLs) that underlie the tractable fragment OWL 2 QL of the Web Ontology Language OWL 2. We start with a novel knowledge evolution framework and natural postulates that evolution should respect, and compare our postulates to the well-established AGM postulates. We then review well-known model and formula-based approaches for expansion and contraction for propositional theories and show how they can be adapted to the case of $\mathit{DL}\text{-}\mathit{Lite}$. In particular, we show intrinsic limitations of model-based approaches: besides the fact that some of them do not respect the postulates we have established, they ignore the structural properties of KBs. This leads to undesired properties of evolution results: evolution of $\mathit{DL}\text{-}\mathit{Lite}$ KBs cannot be captured in $\mathit{DL}\text{-}\mathit{Lite}$. Moreover, we show that well-known formula-based approaches are also not appropriate for $\mathit{DL}\text{-}\mathit{Lite}$ expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in $\mathit{DL}\text{-}\mathit{Lite}$. Thus, we propose a novel formula-based approach that respects our principles and for which evolution is expressible in $\mathit{DL}\text{-}\mathit{Lite}$. For this approach we also propose polynomial time deterministic algorithms to compute evolution of $\mathit{DL}\text{-}\mathit{Lite}$ KBs when evolution affects only factual data.

知识库（KB）并不是静态的实体：新信息不断出现，并且某些先前的知识变得过时。为了反映这种知识的发展，知识库应该用新知识扩展，并从过时的知识中收缩。对于命题，对该问题进行了充分研究，但对于一阶KB，则少得多。在这项工作中，我们调查了以KB表示的知识库的知识扩展和收缩$\mathit{DL}\text{--}\mathit{精简版}$，它是Web本体论语言OWL 2的易处理片段OWL 2 QL底层的描述逻辑系列。我们从一个新颖的知识进化框架开始，自然而然地假设进化应该受到尊重，并将我们的假设与公认的AGM假设。然后，我们对命题理论的扩展和收缩的著名模型和基于公式的方法进行回顾，并说明如何将它们适应于命题理论。$\mathit{DL}\text{--}\mathit{精简版}$。特别是，我们展示了基于模型的方法的固有局限性：除了其中一些不遵守我们已建立的假设的事实外，它们还忽略了知识库的结构特性。这导致了进化结果的不良特性：$\mathit{DL}\text{--}\mathit{精简版}$ 无法捕获KB $\mathit{DL}\text{--}\mathit{精简版}$。此外，我们证明了众所周知的基于公式的方法也不适合$\mathit{DL}\text{--}\mathit{精简版}$ 膨胀和收缩：它们要么具有很高的计算复杂性，要​​么产生无法用逻辑表示的逻辑理论 $\mathit{DL}\text{--}\mathit{精简版}$。因此，我们提出了一种新颖的基于公式的方法，该方法尊重我们的原则，并且可以在其中表达进化$\mathit{DL}\text{--}\mathit{精简版}$。对于这种方法，我们还提出了多项式时间确定性算法来计算$\mathit{DL}\text{--}\mathit{精简版}$ 演化仅影响事实数据时的知识库。