Towards Interactive Causal Relation Discovery Driven by an Ontology

Discovering causal relations in a knowledge base represents nowadays a challenging issue, as it gives a brand new way of understanding complex domains. In this paper, we present a method to combine an ontology with an object-oriented extension of the Bayesian networks (BNs), called probabilistic relational model (PRM), in order to help a user to check his/her assumption on causal relations between data and to discover new relationships. This assumption is also important as it guides the PRM construction and provide a learning under causal constraints.