Learning to Refine Expansion Terms for Biomedical Information Retrieval Using Semantic Resources

With the rapid development of biomedicine, the number of biomedical articles has increased accordingly, which presents a great challenge for biologists trying to keep up with the latest research. Information retrieval seeks to meet this challenge by searching among a large number of articles based on given queries and providing the most relevant ones to fulfill information needs. As an effective information retrieval technique, query expansion has some room for improvement to achieve the desired performance when directly applied for biomedical information retrieval because there exist many domain-related terms both in users' queries and in related articles. To solve this problem, we propose a biomedical query expansion framework based on learning-to-rank methods, in which we refine candidate expansion terms by training term-ranking models to select the most relevant terms. To train the term-ranking models, we first propose a pseudo-relevance feedback method based on MeSH to select candidate expansion terms and then represent the candidate terms as feature vectors by defining both the corpus-based term features and the resource-based term features. Experimental results obtained for TREC genomics datasets show that our method can capture more relevant terms to expand the original query and effectively improve biomedical information retrieval performance.