Relational Bayesian Networks
This work addresses the need for more expressive probabilistic modeling in AI and machine learning, though it appears to be an incremental improvement over existing methods.
The paper tackles the problem of representing probabilistic relations on multiple random events by developing a new method that directly represents probability distributions over relations using Bayesian networks, resulting in a more expressive formalism than previous knowledge-based approaches.
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions.