一句话：用余代数模型为概率逻辑编程及加权逻辑编程建立语义，给出推导树与可能世界解释并恢复分布语义。
概率逻辑编程通过为子句标注概率来扩展逻辑编程，已成为人工智能中处理不确定性的重要形式化工具，但其语义多停留在具体构造层面，缺乏统一的范畴论基础。本文提出概率逻辑编程的余代数语义：将程序建模为关于某函子 F 的余代数，并借助 cofree 余代数给出两种语义。其一，F-余代数直接给出基于推导树的语义；其二，将 F 嵌入另一类型 G，得到程序的「可能世界」解释作为 cofree G-余代数，由此可以恢复概率逻辑编程通常的分布语义。作者进一步表明，类似方法可用于为加权逻辑编程提供余代数语义，从而在统一框架下刻画概率与加权两类逻辑程序的语义结构。
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic semantics on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a `possible worlds' interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming. Furthermore, we show that a similar approach can be used to provide a coalgebraic semantics to weighted logic programming.