Fusion of domain knowledge with data for structural learning in object oriented domains
When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis. In this paper we propose a method for doing structural learning in object oriented domains. It is demonstrated that this method is more efficient than conventional algorithms in such domains, and it is argued that the method supports a natural approach for expressing and incorporating prior information provided by domain experts.
structural learning object orientation bayesian networks knowledge fusion