We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks. To demonstrate the usefulness of such a general approach, we completely characterise the projective families of distributions representable in the generalised distribution semantics and we demonstrate both that large classes of interesting projective families cannot be represented in a generalised distribution semantics and that already a very limited fragment of logic programming (acyclic determinate logic programs) in the determinsitic part suffices to represent all those projective families that are representable in the generalised distribution semantics at all.

翻译：我们通过提炼其核心概念——自由随机分量与确定性部分的分离——对支撑概率逻辑编程的分布语义进行了推广。这一抽象将逻辑编程核心思想扩展到涵盖概率数据库、概率有限模型论及离散提升贝叶斯网络等框架。为论证该通用方法的实用性，我们完整刻画了广义分布语义中可表示的投影分布族特征，并证明：一方面，大量有意义的投影分布族无法在广义分布语义中表示；另一方面，确定性部分仅需极有限的逻辑编程片段（无环确定逻辑程序）即可表示所有在广义分布语义中可表示的投影分布族。