论文标题
临时马尔可夫逻辑网络上推理的参数化
Parameterisation of Reasoning on Temporal Markov Logic Networks
论文作者
论文摘要
我们旨在改善不一致和不确定数据的推理。我们专注于知识图数据，随时间间隔扩展以指定其有效性，如历史科学中经常发现。我们提出了关于语义的原则，以在新的临时马尔可夫逻辑网络（TMLN）上进行有效的最大a-posteriori推断，该推断通过不确定的时间事实和规则扩展了马尔可夫逻辑网络（MLN）。我们检查了时间公式集之间的总时间和部分时间（IN）的一致性关系。然后，我们提出了一种新的时间参数语义，该语义可能结合了几个子功能，从而可以使用不同的评估策略。最后，我们暴露了语义必须尊重以满足我们原则的约束。
We aim at improving reasoning on inconsistent and uncertain data. We focus on knowledge-graph data, extended with time intervals to specify their validity, as regularly found in historical sciences. We propose principles on semantics for efficient Maximum A-Posteriori inference on the new Temporal Markov Logic Networks (TMLN) which extend the Markov Logic Networks (MLN) by uncertain temporal facts and rules. We examine total and partial temporal (in)consistency relations between sets of temporal formulae. Then we propose a new Temporal Parametric Semantics, which may combine several sub-functions, allowing to use different assessment strategies. Finally, we expose the constraints that semantics must respect to satisfy our principles.
