Improving average ranking precision in user searches for biomedical research datasets

TL;DR

This paper presents a novel ranking pipeline for biomedical dataset search that combines query expansion, relevance-aware similarity measures, and dataset categorization, achieving top performance in the bioCADDIE challenge.

Contribution

The authors introduce a new ranking system integrating word embedding-based query expansion, relevance-sensitive similarity measures, and dataset categorization to improve biomedical dataset retrieval.

Findings

Achieved highest infAP in bioCADDIE challenge, +22.3% over median.

Query expansion improved performance by up to +5.0% in infAP.

Similarity measure demonstrated robustness across training conditions.

Abstract

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies.