SemCaDo: A Serendipitous Causal Discovery Algorithm for Ontology Evolution
With the rising need to reuse the existing knowledge when learning Causal Bayesian Networks (CBNs), the ontologies can supply valuable semantic information to make further interesting discoveries with the minimum expected cost and effort. In this paper, the authors propose a cyclic approach in which they make use of the ontology in an interchangeable way. The first direction involves the integration of semantic knowledge to anticipate the optimal choice of experimentations via a serendipitous causal discovery strategy. The second complementary direction concerns an enrichment process by which it will be possible to reuse these causal discoveries, support the evolving character of the semantic background and make an ontology evolution.