An intelligent interface for rule elicitation
Saeed Hassanpour, Amar K. Das · 2011
Rule bases are increasingly being used as knowledge resources for reasoning in Semantic Web applications. How-ever, a major obstacle to the wider use of rule bases is the difficulty of acquiring rules from domain experts. In this work, we present a predictive editing method, also known as autocompletion, to facilitate the elicitation of rules specified in the Semantic Web Rule Language (SWRL). Our method uses six different approaches for predictive editing based on frequency, position, structure, and domain-range information. We have implemented our method as a part of Protégé SWRL editor plug in. Initial usage of our method shows that a combined approach accurately recommends the most relevant rule predicates in the rule specification process.