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.
翻译:我们的目标是改进关于不一致和不确定数据的推理。我们侧重于知识绘图数据,如历史科学中经常发现的那样,时间间隔延长以具体说明其有效性。我们提出了关于对新的Timoral Markov逻辑网络(TMLN)进行有效的A-Posperiorial 最高推论的语义原则,这些网络通过不确定的时间事实和规则扩大了Markov逻辑网络(MLN ) 。我们研究了各套时间公式之间的全部和部分时间(不连贯)关系。然后我们提出了一个新的Temal 参数语义,它可以将几个子功能结合起来,允许使用不同的评估战略。最后,我们暴露了语义必须尊重的制约,以满足我们的原则。