Abstract
Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.
Keywords
- Knowledge Representation and Reasoning: KRR: Reasoning about knowledge and belief
- Knowledge Representation and Reasoning: KRR: Applications
- Multidisciplinary Topics and Applications: MDA: Databases
- Planning and Scheduling: PS: Markov decisions processes
