SNOMED CT Saves Keystrokes: Quantifying Semantic Autocompletion

摘要

In many applications autocompletion functionality saves keystrokes, increases user experience, and helps the user to comply with standardized terminology. Intuitively, the more context information we have about the user, the more accurate autocompletion suggestions we can give. In this paper we research the added value of contextual information for autocompletion algorithms, measured as the average number of saved keystrokes. In our experiments, a context is represented as a set of SNOMED CT terms. Using the structure of SNOMED CT we determine the semantic distance of each SNOMED CT term to the context terms. The resulting distance function is injected in the autocompletion algorithms to reward terms that are semantically close to the context. Our results show that semantic enhancement saves up to 18% of keystrokes, in addition to the percentage of keystrokes saved for the non-semantic base algorithm.