Information retrieval with concept-based pseudo-relevance feedback in MEDLINE

Abstract

Although using domain specific knowledge sources for information retrieval yields more accurate results compared to pure keyword-based methods, more improvements can be achieved by considering both relations between concepts in an ontology and also their statistical dependencies over the corpus. In this paper, an innovative approach named concept-based pseudo-relevance feedback is introduced for improving accuracy of biomedical retrieval systems. Proposed method uses a hybrid retrieval algorithm for discovering relevancy between queries and documents which is based on a combination of keyword- and concept-based approaches. It also uses a pseudo-relevance feedback mechanism for expanding initial queries with auxiliary biomedical concepts extracted from top-ranked results of hybrid information retrieval. Using concept-based similarities makes it possible for the system to detect related documents to users’ queries, which are semantically close to each other while not necessarily sharing common keywords. In addition, expanding initial queries with concepts introduced by pseudo-relevance feedback captures those relations between queries and documents, which rely on statistical dependencies between concepts they contain. As a matter of fact, these relations may remain undetected, examining merely existing links between concepts in an external knowledge source. Proposed approach is evaluated using OHSUMED test collection and standard evaluation methods from text retrieval conference (TREC). Experimental results on MEDLINE documents (in OHSUMED collection) show 21% improvement over keyword-based approach in terms of mean average precision, which is a noticeable gain.