A platform for Okapi-based contextual information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A cross-lingual framework for monolingual biomedical information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
Exploiting medical hierarchies for concept-based information retrieval
Proceedings of the Seventeenth Australasian Document Computing Symposium
A task-specific query and document representation for medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Correlating medical-dependent query features with image retrieval models using association rules
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and go beyond keywords matching. To address these issues, in this paper we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. To model a query context initially we model and develop query domain ontology. The query domain ontology represents concepts closely related with query concepts. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to query concept(s). The query context is then exploited in query expansion and patients records re-ranking for improving clinical retrieval performance. We evaluate our approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model.