A self-configuring semantic decision table For parameterizing an ontology-based data matching strategy

a semantic decision table (SDT), which is a decision table annotated with an ontology (or ontologies), is a means to ensure the completeness and correctness of a decision table. It can be used to store the parameters of a matching strategy. In principle, SDTs can be used to configure any kinds of strategies, functions or algorithm. In this paper, we use an ontology-based data matching strategy, which has been developed and used for competency matching in the fields of human resource management and eLearning/training to demonstrate how SDTs can be used. In particular, we focus on how to make SDT self-configured based on the feedbacks from an end user while evaluating this strategy. We design an algorithm called Semantic Decision Table Self-Configuration Algorithm (SDT-SCA) to find the best parameters that can be stored as action stubs in an SDT. We discuss the design, implementation and industrial experiments concerning SDT-SCA.