Extending OWL with custom relations for knowledge-driven intelligent agents

Abstract

Ontology is one of the popular models for knowledge representation, and Web Ontology Language (OWL) is the current industry standard for supporting ontology in semantic web and knowledge encoding for various application domains including healthcare and cyber-security. But ontology basically only supports one relation “is-a” between the classes. Even though OWL introduced object properties to emulate other relations, it lacks effective support for fundamental relations like part-of, which is very popular in engineering knowledge, and the temporal relation, which underpins all algorithms for most computer science knowledge. This paper introduces our minimal syntax extension to OWL to allow domain experts to declare and apply custom relations with various mathematical properties, and our extension to Stanford University’s Protégé project so that it can be used to encode intuitively knowledge with custom relations, and our PaceJena project that can use the extended OWL documents to empower knowledge-driven decision making in software agents. Important use cases illustrate how this approach supported effective drug side-effect detection, efficient software diagnostic message pattern specification and detection, and an intelligent online tutoring system that supports effective cyberlearning with knowledge navigation, specialization and generalization as well as assessment-based learning.