Reasoning about Unmodelled Concepts - Incorporating Class Taxonomies in Probabilistic Relational Models
This addresses a key bottleneck in probabilistic relational models for AI researchers, offering an incremental improvement by exploiting structure in possible worlds.
The paper tackles the problem of large graphical model sizes in first-order probabilistic methods by proposing fuzzy inference in Markov logic networks to incorporate taxonomic knowledge, resulting in more compact probability distributions and enabling reasoning about unmodelled concepts.
A key problem in the application of first-order probabilistic methods is the enormous size of graphical models they imply. The size results from the possible worlds that can be generated by a domain of objects and relations. One of the reasons for this explosion is that so far the approaches do not sufficiently exploit the structure and similarity of possible worlds in order to encode the models more compactly. We propose fuzzy inference in Markov logic networks, which enables the use of taxonomic knowledge as a source of imposing structure onto possible worlds. We show that by exploiting this structure, probability distributions can be represented more compactly and that the reasoning systems become capable of reasoning about concepts not contained in the probabilistic knowledge base.