Automated Generation of Control Concepts Annotation Rules Using Inductive Logic Programming

Abstract

Capturing domain knowledge is a time-consuming procedure that usually requires the collaboration of a Subject Matter Expert (SME) and a modeling expert to encode the knowledge. This situation is further exacerbated in some domains and applications. The SME may find it challenging to articulate the domain knowledge as a procedure or a set of rules but may find it easier to classify instance data. In the cyber-physical domain, inferring the implemented mathematical concepts in the source code or a different form of representation, such as the Resource Description Framework (RDF), is difficult for the SME, requiring particular expertise in low-level programming or knowledge in Semantic Web technologies. To facilitate this knowledge elicitation from SMEs, we developed a system that automatically generates classification and annotation rules for control concepts in cyber-physical systems (CPS). Our proposed approach leverages the RDF representation of CPS source code and generates the rules using Inductive Logic Programming and semantic technologies. The resulting rules require a small set of labeled instance data that is provided interactively by the SME through a user interface within our system. The generated rules can be inspected, iterated and manually refined.KeywordsKnowledge captureSemantic modelKnowledge graphsRulesRule annotationCyber-physical systems