Objectives: Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances.
Design: Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection.
Measurements: Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests.
Results: Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% +/- 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% +/- 13% (95% CI, 27% to 38%); and one context, 20% +/- 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5.
Conclusions: The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.