Improving understandability of semantic search explanations

Abstract

Explanation-aware software design aims at making software systems smarter in interactions with their users. The long-term goal is to provide methods and tools for systematically engineering understandability into the respective (knowledge-based) software system. In this paper, we describe how we improved a semantic search engine, i.e., RadSem, regarding understandability. The research project MEDICO aims at developing an intelligent, robust and scalable semantic search engine for medical documents. RadSem is based on formal ontologies and designated for different kinds of users. Since semantic search results are often hard to understand, an explanation facility for justifying and exploring search results was integrated into RadSem employing the same ontologies used for searching also for explanation generation. We evaluated the understandability of selected concept labels in an experiment with different user groups using semantic networks as form of depicting explanations and using a class frequency approach for selecting appropriate labels.